anomaly-explain.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explanatory Anomaly Detection on Nuscenes data\n",
    "\n",
    "This system demos the reasonableness monitoring system and anomaly detection through explanations on the Nuscenes dataset.  This code assumes that the database is available at `../data/`, but this can be changed to the directory where the dataset is located.  \n",
    "\n",
    "This is processing on the mini version of the full dataset.  The full dataset processing will soon follow.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======\n",
      "Loading NuScenes tables for version v1.0-mini...\n",
      "23 category,\n",
      "8 attribute,\n",
      "4 visibility,\n",
      "911 instance,\n",
      "12 sensor,\n",
      "120 calibrated_sensor,\n",
      "31206 ego_pose,\n",
      "8 log,\n",
      "10 scene,\n",
      "404 sample,\n",
      "31206 sample_data,\n",
      "18538 sample_annotation,\n",
      "4 map,\n",
      "Done loading in 0.7 seconds.\n",
      "======\n",
      "Reverse indexing ...\n",
      "Done reverse indexing in 0.1 seconds.\n",
      "======\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from nuscenes.nuscenes import NuScenes\n",
    "import pprint as pp\n",
    "from nuscenes.utils.data_classes import *\n",
    "\n",
    "# TODO: this should be changed somewhere \n",
    "data_root = '/Users/leilani/Dropbox (MIT)/car-can-explain/data/'\n",
    "nuscenes_root = data_root+'nuscenes-data/'\n",
    "data_name = 'v1.0-mini'\n",
    "\n",
    "nusc = NuScenes(version=data_name, dataroot=nuscenes_root+data_name, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing that the driver code runs.\n",
    "\n",
    "The driver currently runs the uber example and searches through a commonsense knowledge base (ConceptNet) with verbose messages .  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Verbose output.\n",
      "INFO: Explaining behavior in the vehicle domain\n",
      "\n",
      "DEBUG: commonsense/relations.csv is parsed as a csv file\n",
      "DEBUG:    Relation URI  ...                       Examples\n",
      "0  ------------  ...                       --------\n",
      "1  /r/RelatedTo  ...              learn ↔ erudition\n",
      "2     /r/FormOf  ...                  slept → sleep\n",
      "3        /r/IsA  ...  car → vehicle; Chicago → city\n",
      "4     /r/PartOf  ...                gearshift → car\n",
      "\n",
      "[5 rows x 3 columns]\n",
      "DEBUG: commonsense/anchors.csv is parsed as a csv file\n",
      "DEBUG:    Relation URI  ...                       Examples\n",
      "0  ------------  ...                       --------\n",
      "1  /r/RelatedTo  ...              learn ↔ erudition\n",
      "2     /r/FormOf  ...                  slept → sleep\n",
      "3        /r/IsA  ...  car → vehicle; Chicago → city\n",
      "4     /r/PartOf  ...                gearshift → car\n",
      "\n",
      "[5 rows x 3 columns]\n",
      "SHOULDN'T HAVE\n",
      "DEBUG: searching for an anchor point for unknown_object\n",
      "DEBUG: anchor point object is partof the concept phrase: unknown_object\n",
      "DEBUG: Making a new fact: ['unknown_object', 'IsA', 'object'] with reason: direct search\n",
      "REASONS ARE [['unknown_object IsA object', 'direct search']]\n",
      "DEBUG: searching for an anchor point for vehicle\n",
      "are we in the closest\n",
      "DEBUG: Searching for an IsA link between vehicle and animal\n",
      "DEBUG: Starting new HTTP connection (1): api.conceptnet.io:80\n",
      "DEBUG: http://api.conceptnet.io:80 \"GET /c/en/vehicle?rel=/r/IsA&limit=1000 HTTP/1.1\" 200 None\n",
      "DEBUG: Searching for an IsA link between vehicle and object\n",
      "DEBUG: Starting new HTTP connection (1): api.conceptnet.io:80\n",
      "DEBUG: http://api.conceptnet.io:80 \"GET /c/en/vehicle?rel=/r/IsA&limit=1000 HTTP/1.1\" 200 None\n",
      "DEBUG: Making a new fact: ['vehicle', 'IsA', 'object'] with reason: ConceptNet IsA link\n",
      "REASONS ARE [['unknown_object IsA object', 'direct search'], ['vehicle IsA object', 'ConceptNet IsA link']]\n",
      "DEBUG: searching for an anchor point for bicycle\n",
      "are we in the closest\n",
      "DEBUG: Searching for an IsA link between bicycle and animal\n",
      "DEBUG: Starting new HTTP connection (1): api.conceptnet.io:80\n",
      "DEBUG: http://api.conceptnet.io:80 \"GET /c/en/bicycle?rel=/r/IsA&limit=1000 HTTP/1.1\" 200 None\n",
      "DEBUG: Searching for an IsA link between bicycle and object\n",
      "DEBUG: Starting new HTTP connection (1): api.conceptnet.io:80\n",
      "DEBUG: http://api.conceptnet.io:80 \"GET /c/en/bicycle?rel=/r/IsA&limit=1000 HTTP/1.1\" 200 None\n",
      "DEBUG: Making a new fact: ['bicycle', 'IsA', 'object'] with reason: ConceptNet IsA link\n",
      "REASONS ARE [['unknown_object IsA object', 'direct search'], ['vehicle IsA object', 'ConceptNet IsA link'], ['bicycle IsA object', 'ConceptNet IsA link']]\n",
      "DEBUG: Accessing KB for this concept: unknown_object\n",
      "DEBUG: Starting new HTTP connection (1): api.conceptnet.io:80\n",
      "DEBUG: http://api.conceptnet.io:80 \"GET /c/en/object?rel=/r/AtLocation HTTP/1.1\" 200 None\n",
      "DEBUG: Starting new HTTP connection (1): api.conceptnet.io:80\n",
      "DEBUG: http://api.conceptnet.io:80 \"GET /c/en/object?rel=/r/LocatedNear HTTP/1.1\" 200 None\n",
      "DEBUG: Accessing KB for this concept: vehicle\n",
      "DEBUG: Starting new HTTP connection (1): api.conceptnet.io:80\n",
      "DEBUG: http://api.conceptnet.io:80 \"GET /c/en/vehicle?rel=/r/AtLocation HTTP/1.1\" 200 None\n",
      "DEBUG: Making a new fact: ['vehicle', 'AtLocation', 'the street'] with reason: ConceptNet\n",
      "DEBUG: Starting new HTTP connection (1): api.conceptnet.io:80\n",
      "DEBUG: http://api.conceptnet.io:80 \"GET /c/en/vehicle?rel=/r/LocatedNear HTTP/1.1\" 200 None\n",
      "DEBUG: Accessing KB for this concept: bicycle\n",
      "DEBUG: Starting new HTTP connection (1): api.conceptnet.io:80\n",
      "DEBUG: http://api.conceptnet.io:80 \"GET /c/en/bicycle?rel=/r/AtLocation HTTP/1.1\" 200 None\n",
      "DEBUG: Making a new fact: ['bicycle', 'AtLocation', 'the garage'] with reason: ConceptNet\n",
      "DEBUG: Making a new fact: ['bicycle', 'AtLocation', 'the street'] with reason: ConceptNet\n",
      "DEBUG: Making a new fact: ['bicycle', 'AtLocation', 'a toy store'] with reason: ConceptNet\n",
      "DEBUG: Starting new HTTP connection (1): api.conceptnet.io:80\n",
      "DEBUG: http://api.conceptnet.io:80 \"GET /c/en/bicycle?rel=/r/LocatedNear HTTP/1.1\" 200 None\n",
      "REASONS ARE [['unknown_object IsA object', 'direct search'], ['vehicle IsA object', 'ConceptNet IsA link'], ['bicycle IsA object', 'ConceptNet IsA link'], ['vehicle AtLocation street', 'ConceptNet'], ['bicycle AtLocation garage', 'ConceptNet'], ['bicycle AtLocation street', 'ConceptNet'], ['bicycle AtLocation toy_store', 'ConceptNet']]\n",
      "DEBUG: [['unknown_object IsA object', 'direct search'], ['vehicle IsA object', 'ConceptNet IsA link'], ['bicycle IsA object', 'ConceptNet IsA link'], ['vehicle AtLocation street', 'ConceptNet'], ['bicycle AtLocation garage', 'ConceptNet'], ['bicycle AtLocation street', 'ConceptNet'], ['bicycle AtLocation toy_store', 'ConceptNet']]\n",
      "DEBUG: Snapshot monitor made with the following data:                            fact               reason\n",
      "0     unknown_object IsA object        direct search\n",
      "1            vehicle IsA object  ConceptNet IsA link\n",
      "2            bicycle IsA object  ConceptNet IsA link\n",
      "3     vehicle AtLocation street           ConceptNet\n",
      "4     bicycle AtLocation garage           ConceptNet\n",
      "5     bicycle AtLocation street           ConceptNet\n",
      "6  bicycle AtLocation toy_store           ConceptNet\n",
      "DEBUG: 0       unknown_object IsA object\n",
      "1              vehicle IsA object\n",
      "2              bicycle IsA object\n",
      "3       vehicle AtLocation street\n",
      "4       bicycle AtLocation garage\n",
      "5       bicycle AtLocation street\n",
      "6    bicycle AtLocation toy_store\n",
      "Name: fact, dtype: object\n",
      "DEBUG: ['unknown_object IsA object', 'vehicle IsA object', 'bicycle IsA object', 'vehicle AtLocation street', 'bicycle AtLocation garage', 'bicycle AtLocation street', 'bicycle AtLocation toy_store']\n",
      "DEBUG: 0       unknown_object IsA object\n",
      "1              vehicle IsA object\n",
      "2              bicycle IsA object\n",
      "3       vehicle AtLocation street\n",
      "4       bicycle AtLocation garage\n",
      "5       bicycle AtLocation street\n",
      "6    bicycle AtLocation toy_store\n",
      "Name: fact, dtype: object\n",
      "DEBUG: ['unknown_object IsA object', 'vehicle IsA object', 'bicycle IsA object', 'vehicle AtLocation street', 'bicycle AtLocation garage', 'bicycle AtLocation street', 'bicycle AtLocation toy_store']\n",
      "DEBUG: new data is                                  fact               reason\n",
      "0           unknown_object IsA object        direct search\n",
      "1                  vehicle IsA object  ConceptNet IsA link\n",
      "2                  bicycle IsA object  ConceptNet IsA link\n",
      "3           vehicle AtLocation street           ConceptNet\n",
      "4           bicycle AtLocation garage           ConceptNet\n",
      "5           bicycle AtLocation street           ConceptNet\n",
      "6        bicycle AtLocation toy_store           ConceptNet\n",
      "7   unknown_object consistent vehicle    Anchor rule fired\n",
      "8   unknown_object consistent bicycle    Anchor rule fired\n",
      "9   vehicle consistent unknown_object    Anchor rule fired\n",
      "10         vehicle consistent bicycle    Anchor rule fired\n",
      "11  bicycle consistent unknown_object    Anchor rule fired\n",
      "12         bicycle consistent vehicle    Anchor rule fired\n",
      "DEBUG: 0             unknown_object IsA object\n",
      "1                    vehicle IsA object\n",
      "2                    bicycle IsA object\n",
      "3             vehicle AtLocation street\n",
      "4             bicycle AtLocation garage\n",
      "5             bicycle AtLocation street\n",
      "6          bicycle AtLocation toy_store\n",
      "7     unknown_object consistent vehicle\n",
      "8     unknown_object consistent bicycle\n",
      "9     vehicle consistent unknown_object\n",
      "10           vehicle consistent bicycle\n",
      "11    bicycle consistent unknown_object\n",
      "12           bicycle consistent vehicle\n",
      "Name: fact, dtype: object\n",
      "DEBUG: ['unknown_object IsA object', 'vehicle IsA object', 'bicycle IsA object', 'vehicle AtLocation street', 'bicycle AtLocation garage', 'bicycle AtLocation street', 'bicycle AtLocation toy_store', 'unknown_object consistent vehicle', 'unknown_object consistent bicycle', 'vehicle consistent unknown_object', 'vehicle consistent bicycle', 'bicycle consistent unknown_object', 'bicycle consistent vehicle']\n",
      "DEBUG: new data is                                  fact               reason\n",
      "0           unknown_object IsA object        direct search\n",
      "1                  vehicle IsA object  ConceptNet IsA link\n",
      "2                  bicycle IsA object  ConceptNet IsA link\n",
      "3           vehicle AtLocation street           ConceptNet\n",
      "4           bicycle AtLocation garage           ConceptNet\n",
      "5           bicycle AtLocation street           ConceptNet\n",
      "6        bicycle AtLocation toy_store           ConceptNet\n",
      "7   unknown_object consistent vehicle    Anchor rule fired\n",
      "8   unknown_object consistent bicycle    Anchor rule fired\n",
      "9   vehicle consistent unknown_object    Anchor rule fired\n",
      "10         vehicle consistent bicycle    Anchor rule fired\n",
      "11  bicycle consistent unknown_object    Anchor rule fired\n",
      "12         bicycle consistent vehicle    Anchor rule fired\n",
      "13       vehicle sameLocation bicycle  Location rule fired\n",
      "14       bicycle sameLocation vehicle  Location rule fired\n",
      "Are we here in reasonable (unknown_object, vehicle, bicycle)\n",
      "DEBUG: [unknown_object, vehicle, bicycle]\n",
      "DEBUG: ['unknown_object IsA object', 'vehicle IsA object', 'bicycle IsA object', 'vehicle AtLocation street', 'bicycle AtLocation garage', 'bicycle AtLocation street', 'bicycle AtLocation toy_store', 'unknown_object consistent vehicle', 'unknown_object consistent bicycle', 'vehicle consistent unknown_object', 'vehicle consistent bicycle', 'bicycle consistent unknown_object', 'bicycle consistent vehicle', 'vehicle sameLocation bicycle', 'bicycle sameLocation vehicle']\n",
      "DEBUG: [unknown_object, vehicle, bicycle]\n",
      "DEBUG: ['unknown_object IsA object', 'vehicle IsA object', 'bicycle IsA object', 'vehicle AtLocation street', 'bicycle AtLocation garage', 'bicycle AtLocation street', 'bicycle AtLocation toy_store', 'unknown_object consistent vehicle', 'unknown_object consistent bicycle', 'vehicle consistent unknown_object', 'vehicle consistent bicycle', 'bicycle consistent unknown_object', 'bicycle consistent vehicle', 'vehicle sameLocation bicycle', 'bicycle sameLocation vehicle']\n",
      "DEBUG: not met unknown_object sameLocation vehicle\n",
      "DEBUG: [unknown_object, vehicle, bicycle]\n",
      "DEBUG: ['unknown_object IsA object', 'vehicle IsA object', 'bicycle IsA object', 'vehicle AtLocation street', 'bicycle AtLocation garage', 'bicycle AtLocation street', 'bicycle AtLocation toy_store', 'unknown_object consistent vehicle', 'unknown_object consistent bicycle', 'vehicle consistent unknown_object', 'vehicle consistent bicycle', 'bicycle consistent unknown_object', 'bicycle consistent vehicle', 'vehicle sameLocation bicycle', 'bicycle sameLocation vehicle']\n",
      "DEBUG: not met unknown_object sameLocation vehicle, but it's an object so it's ok\n",
      "DEBUG: not met unknown_object sameLocation bicycle, but it's an object so it's ok\n",
      "DEBUG: False\n",
      "DEBUG: This vision system is unreasonable.  There is no commonsense supporting the similarity between unknown_object, vehicle, bicycle.  This component should be ignored.\n",
      "DEBUG: reasonable\n",
      "DEBUG: (object_moving, 5_ft_tall, top_left_quadrant)\n",
      "DEBUG: reasonable\n",
      "DEBUG: (moving_quickly, proceed_straight, has_been_straight)\n",
      "explanation : ((False, 'This vision system is unreasonable.  There is no commonsense supporting the similarity between unknown_object, vehicle, bicycle.  This component should be ignored.'), (reasonable, (object_moving, 5_ft_tall, top_left_quadrant)), (reasonable, (moving_quickly, proceed_straight, has_been_straight)))\n",
      "\n",
      "processing time (in seconds): 0.5033719999999997\n"
     ]
    }
   ],
   "source": [
    "!python3 driver.py -v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Gentle Introduction to nuScenes (from tutorial)\n",
    "\n",
    "Here are the top-down introduction of our database. This section is an elaboration of `schema.md`. The dataset comprises of elemental building blocks that are the following:\n",
    "\n",
    "1. `scene` - 20 second snippet of a car's journey.\n",
    "2. `sample` - An annotated snapshot of a scene at a particular timestamp.\n",
    "3. `sample_data` - Data collected from a particular sensor.\n",
    "4. `sample_annotation` - An annotated instance of an object within our interest.\n",
    "5. `instance` - Enumeration of all object instance we observed.\n",
    "6. `category` - Taxonomy of object categories (e.g. vehicle, human). \n",
    "7. `attribute` - Property of an instance that can change while the category remains the same.\n",
    "8. `visibility` - Fraction of pixels visible in all the images collected from 6 different cameras.. \n",
    "9. `sensor` - A specific sensor type.\n",
    "10. `calibrated sensor` - Definition of a particular sensor as calibrated on a particular vehicle.\n",
    "11. `ego_pose` - Ego vehicle poses at a particular timestamp.\n",
    "12. `log` - Log information from which the data was extracted.\n",
    "13. `map` - Map data that is stored as binary semantic masks from a top-down view."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. `scene`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nuScenes is a large scale database that features annotated samples across ***1000 scenes*** of approximately 20 seconds each. Let's take a look at the scenes that we have in the loaded database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scene-0061, Parked truck, construction, intersectio... [18-07-24 03:28:47]   19s, singapore-onenorth, #anns:4622\n",
      "scene-0103, Many peds right, wait for turning car, ... [18-08-01 19:26:43]   19s, boston-seaport, #anns:2046\n",
      "scene-0655, Parking lot, parked cars, jaywalker, be... [18-08-27 15:51:32]   20s, boston-seaport, #anns:2332\n",
      "scene-0553, Wait at intersection, bicycle, large tr... [18-08-28 20:48:16]   20s, boston-seaport, #anns:1950\n",
      "scene-0757, Arrive at busy intersection, bus, wait ... [18-08-30 19:25:08]   20s, boston-seaport, #anns:592\n",
      "scene-0796, Scooter, peds on sidewalk, bus, cars, t... [18-10-02 02:52:24]   20s, singapore-queensto, #anns:708\n",
      "scene-0916, Parking lot, bicycle rack, parked bicyc... [18-10-08 07:37:13]   20s, singapore-queensto, #anns:2387\n",
      "scene-1077, Night, big street, bus stop, high speed... [18-11-21 11:39:27]   20s, singapore-hollandv, #anns:890\n",
      "scene-1094, Night, after rain, many peds, PMD, ped ... [18-11-21 11:47:27]   19s, singapore-hollandv, #anns:1762\n",
      "scene-1100, Night, peds in sidewalk, peds cross cro... [18-11-21 11:49:47]   19s, singapore-hollandv, #anns:935\n"
     ]
    }
   ],
   "source": [
    "nusc.list_scenes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at a scene metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['parked_truck', 'construction', 'intersection', 'turn_left', 'following_a_van']\n",
      "['arrive_at_busy_intersection', 'bus', 'wait_at_intersection', 'bicycle', 'peds']\n"
     ]
    }
   ],
   "source": [
    "first_scene = nusc.scene[0]\n",
    "fourth_scene = nusc.scene[4]\n",
    "first_scene\n",
    "\n",
    "def get_description(scene):\n",
    "    \"\"\"\n",
    "    Returns a list of the descriptions of a particular scene\n",
    "    Underscores make it easier to query the commonsense knowledge base\n",
    "    \"\"\"\n",
    "    text_description = scene['description']\n",
    "    descriptions = text_description.split(',')\n",
    "    description_list = []\n",
    "    for label in descriptions:\n",
    "        # for conceptNet debugging\n",
    "        description_list.append(label.strip().replace(' ', '_').lower())\n",
    "    return description_list\n",
    "\n",
    "print(get_description(first_scene))\n",
    "print(get_description(fourth_scene))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nuscenes Analysis and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame at 2018-07-23 23:28:48.147847 has 78 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:48.698048 has 86 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:49.197353 has 94 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:49.697797 has 103 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:50.152601 has 103 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:50.648325 has 110 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:51.198511 has 118 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:51.697833 has 120 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:52.197715 has 124 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:52.647284 has 127 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:53.146604 has 129 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:53.646462 has 136 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:54.146351 has 137 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:54.696555 has 141 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:55.197541 has 143 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:55.697945 has 147 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:56.198962 has 148 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:56.699359 has 152 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:57.198682 has 156 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:57.698000 has 154 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:58.197897 has 151 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:58.647444 has 148 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:59.147334 has 144 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:28:59.697511 has 136 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:00.197958 has 125 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:00.797343 has 122 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:01.297218 has 122 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:01.797653 has 123 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:02.197463 has 125 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:02.697870 has 119 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:03.197769 has 116 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:03.697655 has 113 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:04.198088 has 113 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:04.697931 has 107 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:05.197276 has 101 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:05.697148 has 95 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:06.297085 has 87 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
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      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Frame at 2018-07-23 23:29:06.797517 has 77 labeled objects\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "We are monitoring a frame\n",
      "Finished explaining scene at 2018-07-23 23:29:06.797517 with Parked truck, construction, intersection, turn left, following a van\n",
      "Summary of explanations at scene level with 225 distinct annotations\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "NORMALIZE = 1000000.0\n",
    "import commonsense.conceptnet as kb\n",
    "import monitor.reasonableness_monitor as monitor\n",
    "\n",
    "anchors = ['animal', 'object', 'place', 'plant']\n",
    "relations = ['AtLocation', 'LocatedNear'] # technically these are vehicle relations for now\n",
    "\n",
    "def iterate_scenes(limit=1, debug=False):\n",
    "    \"\"\"\n",
    "    This will go through all the scenes in a particular dataset.\n",
    "    Currently limit is set to 1 for testing.\n",
    "    \n",
    "    Assumes that scenes are independent (instances are not tracked across scenes)\n",
    "    \"\"\"\n",
    "    for index in range(0,limit):\n",
    "        if debug:\n",
    "            print(\"Processing Scene %d\"%index)\n",
    "        explain_scene(nusc.scene[index], debug)\n",
    "\n",
    "def explain_scene(scene, debug=False):\n",
    "    raw_data = {}\n",
    "    first_token = scene['first_sample_token']\n",
    "    frame = nusc.get('sample', first_token)\n",
    "    time = convert_time(frame['timestamp'])\n",
    "    sizes = []\n",
    "    \n",
    "    frame_tokens = [first_token]\n",
    "    \n",
    "    while frame['next']: # is not None:\n",
    "        token = frame['next']\n",
    "        #print(\"getting data for %s\"%token)\n",
    "        frame = nusc.get('sample', token)\n",
    "        populate_frame_data(frame, time, raw_data, sizes, debug) # This updates the raw_data \n",
    "        time = convert_time(frame['timestamp'])\n",
    "        frame_tokens.append(token)\n",
    "    #print(\"Finished explaining scene at time=%d seconds with %s\"%(time, scene['token'], scene['description']))\n",
    "    if debug:\n",
    "        print(\"Finished explaining scene at %s with %s\"%(time, scene['description']))\n",
    "        print(\"Summary of explanations at scene level with %d distinct annotations\"%len(raw_data))\n",
    "\n",
    "def populate_frame_data(frame, time, raw_data, sizes, debug=False):\n",
    "    \"\"\"\n",
    "    Go through all the annotations for a particular frame\n",
    "    \"\"\"\n",
    "    if debug:\n",
    "        print(\"Frame at %s has %d labeled objects\" %( convert_time(frame['timestamp']), len(frame['anns'])))\n",
    "    \n",
    "    #raw_data = {}\n",
    "    for ann_token in frame['anns']:\n",
    "        ann_data = nusc.get('sample_annotation', ann_token)\n",
    "        instance = ann_data['instance_token']\n",
    "        # Need category, size, translation\n",
    "        # Might also what to store keys\n",
    "        # Split into LiDAR and Vision \n",
    "        \n",
    "        # token, sample_token, instance_token, visibility_token, if attribute_tokens\n",
    "        # translation, size, prev and next --> Which ones are they referring to\n",
    "        frame_explanation = monitor_frame(ann_data, sizes)\n",
    "        results = (ann_data, frame_explanation)\n",
    "        if instance not in raw_data:\n",
    "            raw_data[instance] = [results]\n",
    "            raw_data[instance].append(results)\n",
    "        #raw_data.append([ann_data['category_name'], ann_data['visibility_token'], ann_data['translation'], ann_data['size']])\n",
    "    #print(raw_data)\n",
    "    #track_annotation(ann_data['instance_token'], ann_data['next'])\n",
    "\n",
    "keys = ['visibility_token', 'size', 'translation', 'category_name']\n",
    "    \n",
    "def monitor_frame(ann_data, size):\n",
    "    print(\"We are monitoring a frame\")\n",
    "    #print(ann_data)\n",
    "    #print({k:d[k] for k in l if k in d})\n",
    "    #print({k:ann_data[k] for k in ann_data if k in keys})\n",
    "    # Convert translation / size to feet and take away ego pose.\n",
    "    #print(ann_data)\n",
    "    #temp = monitor.snapshot_monitor(ann_data, anchors, relations, False)\n",
    "    #print(\"Finished %s monitoring a frame\", temp)\n",
    "    return\n",
    "\n",
    "def track_annotation(instance_token, next_token):\n",
    "    instance = nusc.get('instance', instance_token)\n",
    "    #print(instance)\n",
    "    annotation = nusc.get('sample_annotation', next_token)\n",
    "    #print(\"In tracking and annotation is %s\" %annotation)\n",
    "    describe(annotation, nusc.get('sample', annotation['sample_token']))\n",
    "    \n",
    "    while not annotation['next'] == '':\n",
    "        next_annotation = nusc.get('sample_annotation', annotation['next'])\n",
    "        # print(next_annotation)\n",
    "        annotation = next_annotation\n",
    "        describe(annotation, nusc.get('sample', annotation['sample_token']))\n",
    "    print(\"Done processing annotation\")\n",
    "    return\n",
    "\n",
    "def convert_time(unix_time):\n",
    "    return datetime.datetime.fromtimestamp(unix_time/NORMALIZE)\n",
    "\n",
    "def describe(annotation, sample):\n",
    "    print(\"%s found at time %s\"%(annotation['category_name'], convert_time(sample['timestamp'])))\n",
    "\n",
    "explain_scene(first_scene, True) # Debugging is on for now"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['parked_truck', 'construction', 'intersection', 'turn_left', 'following_a_van']\n",
      "['many_peds_right', 'wait_for_turning_car', 'long_bike_rack_left', 'cyclist']\n",
      "['wait_at_intersection', 'bicycle', 'large_truck', 'peds_crossing_crosswalk', 'ped_with_stroller']\n",
      "['parking_lot', 'parked_cars', 'jaywalker', 'bendy_bus', 'gardening_vehicles']\n",
      "['arrive_at_busy_intersection', 'bus', 'wait_at_intersection', 'bicycle', 'peds']\n",
      "['scooter', 'peds_on_sidewalk', 'bus', 'cars', 'truck', 'fake_construction_worker', 'bicycle', 'cross_intersection', 'car_overtaking_us']\n",
      "['parking_lot', 'bicycle_rack', 'parked_bicycles', 'bus', 'many_peds', 'parked_scooters', 'parked_motorcycle']\n",
      "['night', 'big_street', 'bus_stop', 'high_speed', 'construction_vehicle']\n",
      "['night', 'after_rain', 'many_peds', 'pmd', 'ped_with_bag', 'jaywalker', 'truck', 'scooter']\n",
      "['night', 'peds_in_sidewalk', 'peds_cross_crosswalk', 'scooter', 'pmd', 'difficult_lighting']\n"
     ]
    }
   ],
   "source": [
    "total_scenes = len(nusc.scene)\n",
    "total_scenes\n",
    "\n",
    "for item in nusc.scene:\n",
    "    print(get_description(item))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'token': '83d881a6b3d94ef3a3bc3b585cc514f8',\n",
       " 'sample_token': 'ca9a282c9e77460f8360f564131a8af5',\n",
       " 'instance_token': 'e91afa15647c4c4994f19aeb302c7179',\n",
       " 'visibility_token': '4',\n",
       " 'attribute_tokens': ['58aa28b1c2a54dc88e169808c07331e3'],\n",
       " 'translation': [409.989, 1164.099, 1.623],\n",
       " 'size': [2.877, 10.201, 3.595],\n",
       " 'rotation': [-0.5828819500503033, 0.0, 0.0, 0.812556848660791],\n",
       " 'prev': '',\n",
       " 'next': 'f3721bdfd7ee4fd2a4f94874286df471',\n",
       " 'num_lidar_pts': 495,\n",
       " 'num_radar_pts': 13,\n",
       " 'category_name': 'vehicle.truck'}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def volume(coordinates):\n",
    "    product = reduce((lambda x, y: x * y), coordinates)\n",
    "    return product\n",
    "\n",
    "def find_biggest_box(sample):\n",
    "    annotation_tokens = sample['anns']\n",
    "    biggest = None\n",
    "    for token in annotation_tokens:\n",
    "        data = nusc.get('sample_annotation', token)\n",
    "        if biggest is None:\n",
    "            biggest = data\n",
    "        if  volume(data['size'])>= volume(biggest['size']):\n",
    "            biggest = data\n",
    "        #print(data['size'], data['category_name'])\n",
    "    #biggest = max(lis, key=lambda x:int(x))\n",
    "    return biggest\n",
    "large_box_sample = find_biggest_box(first_sample)\n",
    "large_box_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vehicle.truck\n",
      "[('vehicle.trailer', (10.088409224444403, 19.883985914698215)),\n",
      " ('vehicle.bus.bendy', (10.164950236688224, 18.72053552020357)),\n",
      " ('vehicle.bus.rigid', (12.295043974351778, 23.221938518693605)),\n",
      " ('static_object.bicycle_rack', (31.51571844444446, 67.73234406581483)),\n",
      " ('vehicle.truck', (37.87298901880101, 65.42927188850359)),\n",
      " ('vehicle.construction', (42.61961332002291, 71.33850856696627)),\n",
      " ('vehicle.car', (56.40658874343442, 90.47394538439147)),\n",
      " ('vehicle.motorcycle', (71.16700015054927, 103.5583495960536)),\n",
      " ('human.pedestrian.personal_mobility', (71.35951299999999, 103.524824319)),\n",
      " ('vehicle.bicycle', (71.77516392096395, 103.89112509993872)),\n",
      " ('movable_object.barrier', (71.8435782189733, 104.01149951437664)),\n",
      " ('human.pedestrian.construction_worker',\n",
      "  (73.29255058549222, 104.59009109907474)),\n",
      " ('human.pedestrian.adult', (73.36616498622871, 104.623566617278)),\n",
      " ('human.pedestrian.police_officer', (74.149974, 104.997427605)),\n",
      " ('movable_object.pushable_pullable', (74.62185676635931, 105.09235645724306)),\n",
      " ('movable_object.debris', (74.90437299999999, 105.22783326300001)),\n",
      " ('human.pedestrian.child', (74.91238625519848, 105.22388798948245)),\n",
      " ('movable_object.trafficcone', (75.44229432682565, 105.34421338465172))]\n"
     ]
    }
   ],
   "source": [
    "# Might not be correct\n",
    "def surface_diff(x1,x2,y1,y2):\n",
    "    return abs(x1-x2)*max(y1,y2)+min(x1,x2)*abs(y1-y2)\n",
    "\n",
    "def box_diff(box1, box2):\n",
    "    [x1,y1,z1] = box1\n",
    "    [x2,y2,z2] = box2\n",
    "    return surface_diff(x1,x2,y1,y2)+surface_diff(x1,x2,z1,z2)+surface_diff(y1,y2,z1,z2)\n",
    "\n",
    "def volume_diff(box1, box2):\n",
    "    return abs(volume(box1)-volume(box2))\n",
    "\n",
    "# Current trained on the data set\n",
    "# Does aggregate statistics on the label\n",
    "# Returns a dictionary of the label, and it's average size\n",
    "def get_categories():\n",
    "    categories = dict()\n",
    "    \n",
    "    # Add all annotations\n",
    "    for record in nusc.sample_annotation:\n",
    "        if record['category_name'] not in categories:\n",
    "            categories[record['category_name']] = []\n",
    "        categories[record['category_name']].append(record['size'] + [record['size'][1]/ record['size'][0]])\n",
    "    \n",
    "    category_size = dict()\n",
    "    for name, stats in sorted(categories.items()):\n",
    "        stats = np.array(stats)\n",
    "        box = [np.mean(stats[:,0]), np.mean(stats[:,1]), np.mean(stats[:,2])]\n",
    "        category_size[name] = box\n",
    "    return category_size\n",
    "\n",
    "def k_similar_boxes(box, categories):\n",
    "    diffed_categories = dict()\n",
    "    for category in categories:\n",
    "        diffed_categories[category] = (box_diff(box, categories[category]), volume_diff(box, categories[category]))\n",
    "    sorted_x = sorted(diffed_categories.items(), key=lambda kv: kv[1])\n",
    "    return sorted_x\n",
    "\n",
    "my_annotation_token = first_sample['anns'][1]\n",
    "first_sample\n",
    "\n",
    "my_annotation_metadata =  nusc.get('sample_annotation', my_annotation_token)\n",
    "my_annotation_metadata\n",
    "first_box = my_annotation_metadata['size']\n",
    "test_box = large_box_sample\n",
    "\n",
    "#print(my_annotation_metadata['category_name'])\n",
    "#pp.pprint(k_similar_boxes(first_box, get_categories()))\n",
    "\n",
    "print(test_box['category_name'])\n",
    "pp.pprint(k_similar_boxes(test_box['size'], get_categories()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. `category`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `category` is the object assignment of an annotation.  Let's look at the category table we have in our database. The table contains the taxonomy of different object categories and also list the subcategories (delineated by a period). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Category stats for split v1.0-mini:\n",
      "human.pedestrian.adult      n= 4765, width= 0.68±0.11, len= 0.73±0.17, height= 1.76±0.12, lw_aspect= 1.08±0.23\n",
      "human.pedestrian.child      n=   46, width= 0.46±0.08, len= 0.45±0.09, height= 1.37±0.06, lw_aspect= 0.97±0.05\n",
      "human.pedestrian.constructi n=  193, width= 0.69±0.07, len= 0.74±0.12, height= 1.78±0.05, lw_aspect= 1.07±0.16\n",
      "human.pedestrian.personal_m n=   25, width= 0.83±0.00, len= 1.28±0.00, height= 1.87±0.00, lw_aspect= 1.55±0.00\n",
      "human.pedestrian.police_off n=   11, width= 0.59±0.00, len= 0.47±0.00, height= 1.81±0.00, lw_aspect= 0.80±0.00\n",
      "movable_object.barrier      n= 2323, width= 2.32±0.49, len= 0.61±0.11, height= 1.06±0.10, lw_aspect= 0.28±0.09\n",
      "movable_object.debris       n=   13, width= 0.43±0.00, len= 1.43±0.00, height= 0.46±0.00, lw_aspect= 3.35±0.00\n",
      "movable_object.pushable_pul n=   82, width= 0.51±0.06, len= 0.79±0.10, height= 1.04±0.20, lw_aspect= 1.55±0.18\n",
      "movable_object.trafficcone  n= 1378, width= 0.47±0.14, len= 0.45±0.07, height= 0.78±0.13, lw_aspect= 0.99±0.12\n",
      "static_object.bicycle_rack  n=   54, width= 2.67±1.46, len=10.09±6.19, height= 1.40±0.00, lw_aspect= 5.97±4.02\n",
      "vehicle.bicycle             n=  243, width= 0.64±0.12, len= 1.82±0.14, height= 1.39±0.34, lw_aspect= 2.94±0.41\n",
      "vehicle.bus.bendy           n=   57, width= 2.83±0.09, len= 9.23±0.33, height= 3.32±0.07, lw_aspect= 3.27±0.22\n",
      "vehicle.bus.rigid           n=  353, width= 2.95±0.26, len=11.46±1.79, height= 3.80±0.62, lw_aspect= 3.88±0.57\n",
      "vehicle.car                 n= 7619, width= 1.92±0.16, len= 4.62±0.36, height= 1.69±0.21, lw_aspect= 2.41±0.18\n",
      "vehicle.construction        n=  196, width= 2.58±0.35, len= 5.57±1.57, height= 2.38±0.33, lw_aspect= 2.18±0.62\n",
      "vehicle.motorcycle          n=  471, width= 0.68±0.21, len= 1.95±0.38, height= 1.47±0.20, lw_aspect= 3.00±0.62\n",
      "vehicle.trailer             n=   60, width= 2.28±0.08, len=10.14±5.69, height= 3.71±0.27, lw_aspect= 4.37±2.41\n",
      "vehicle.truck               n=  649, width= 2.35±0.34, len= 6.50±1.56, height= 2.62±0.68, lw_aspect= 2.75±0.37\n"
     ]
    }
   ],
   "source": [
    "nusc.list_categories()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A category record contains the name and the description of that particular category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refer to `instructions.md` for the definitions of the different categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nusc.render_annotation(my_sample['anns'][22])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def nuscenes_test(sweeps=5):\n",
    "    lidar_data = nusc.get('sample_data', my_sample['data']['LIDAR_TOP'])\n",
    "    #pp.pprint(lidar_data['calibrated_sensor_token'])\n",
    "    file_path = nuscenes_root+data_name+'/'+lidar_data['filename']\n",
    "    points = LidarPointCloud.from_file(file_path)\n",
    "   # pp.pprint(points.points)\n",
    "#    pc.render_intensity()\n",
    "    nusc.render_sample_data(my_sample['data']['LIDAR_TOP'], nsweeps=1,\n",
    "                            underlay_map=False)\n",
    "nuscenes_test(1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}