TensorFlow_ObjectDetection_Demo_WashingtonDC_TrafficNetwork.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Object Detection Demo: City Network of Capture Points\n",
    "Welcome to the object detection inference walkthrough!  This notebook will walk you step by step through the process of using a pre-trained model to detect objects in an image. Make sure to follow the [installation instructions](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/installation.md) before you start."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import six.moves.urllib as urllib\n",
    "import sys\n",
    "import tarfile\n",
    "import tensorflow as tf\n",
    "import zipfile\n",
    "import getpass\n",
    "import shutil\n",
    "\n",
    "from collections import defaultdict\n",
    "from io import StringIO\n",
    "from matplotlib import pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "from PIL import ImageGrab\n",
    "import time\n",
    "import pandas as pd\n",
    "\n",
    "import cv2\n",
    "\n",
    "import requests\n",
    "from io import BytesIO\n",
    "\n",
    "from copy import deepcopy\n",
    "\n",
    "import scipy.misc\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import arcgis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Env setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This is needed to display the images.\n",
    "%matplotlib inline\n",
    "\n",
    "# This is needed since the notebook is stored in the object_detection folder.\n",
    "sys.path.append(\"..\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Establish Connection to ArcGIS Online via ArcGIS API for Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Authenticate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempting to log in to 'https://esrifederal.maps.arcgis.com'...\n",
      "Please sign in to your GIS and paste the code that is obtained below.\n",
      "If a web browser does not automatically open, please navigate to the URL below yourself instead.\n",
      "Opening web browser to navigate to: https://esrifederal.maps.arcgis.com/sharing/rest/oauth2/authorize?client_id=wt3QUR1M4eum0TVI&response_type=code&expiration=-1&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob\n",
      "Enter code obtained on signing in using SAML: ········\n",
      "Successfully logged in as: albe9057@esri.com_esrifederal\n"
     ]
    }
   ],
   "source": [
    "portals_dict = {\n",
    "    \"esrifederal_gis\": r\"https://esrifederal.maps.arcgis.com\",\n",
    "    \"natgov_gis\": r\"http://esri-natgov105.eastus.cloudapp.azure.com/arcgis\",\n",
    "    \"dot_gis\": r\"http://dot.esri.com/portal\",\n",
    "    \"dev_gis\": r\"http://govdev.eastus.cloudapp.azure.com/arcgis\",\n",
    "    \"local_gis\": r\"https://anieto.esri.com/arcgis\",\n",
    "    \"brett_gis\": r\"https://agsbase.dcwebgis.net/portal\",\n",
    "    \"bill_gis\": r\"https://idt.esri.com/portal\"\n",
    "}\n",
    "\n",
    "gis_url = portals_dict[\"esrifederal_gis\"]\n",
    "\n",
    "if gis_url == portals_dict[\"esrifederal_gis\"]:\n",
    "    gis_app_id = r\"wt3QUR1M4eum0TVI\"\n",
    "    print(\"Attempting to log in to '{0}'...\".format(gis_url))\n",
    "    gis = arcgis.gis.GIS(gis_url, client_id=gis_app_id)\n",
    "    print(\"Successfully logged in as: \" + gis.properties.user.username)\n",
    "else:\n",
    "    gis_username = getpass.getpass(prompt=\"Username: \")\n",
    "    gis_pw = getpass.getpass(prompt=\"Password: \")\n",
    "    print(\"Attempting to log in to '{0}'...\".format(gis_url))\n",
    "    gis = arcgis.gis.GIS(gis_url, gis_username, gis_pw, verify_cert=False)\n",
    "    print(\"Successfully logged in as: \" + gis.properties.user.username)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object detection imports\n",
    "Here are the imports from the object detection module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils import label_map_util\n",
    "\n",
    "from utils import visualization_utils as vis_util"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model preparation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variables\n",
    "\n",
    "Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.  \n",
    "\n",
    "By default we use an \"SSD with Mobilenet\" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model: SSD Mobilenet on COCO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# What model to download.\n",
    "MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'\n",
    "MODEL_FILE = MODEL_NAME + '.tar.gz'\n",
    "DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'\n",
    "\n",
    "# Path to frozen detection graph. This is the actual model that is used for the object detection.\n",
    "PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'\n",
    "\n",
    "# List of the strings that is used to add correct label for each box.\n",
    "PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')\n",
    "\n",
    "NUM_CLASSES = 90"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load a (frozen) Tensorflow model into memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "detection_graph = tf.Graph()\n",
    "with detection_graph.as_default():\n",
    "    od_graph_def = tf.GraphDef()\n",
    "    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n",
    "        serialized_graph = fid.read()\n",
    "        od_graph_def.ParseFromString(serialized_graph)\n",
    "        tf.import_graph_def(od_graph_def, name='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading label map\n",
    "Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "label_map = label_map_util.load_labelmap(PATH_TO_LABELS)\n",
    "categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\n",
    "category_index = label_map_util.create_category_index(categories)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_image_into_numpy_array(image):\n",
    "  (im_width, im_height) = image.size\n",
    "  return np.array(image.getdata()).reshape(\n",
    "      (im_height, im_width, 3)).astype(np.uint8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a helper function that takes the detection graph output tensor (np arrays), stacks the classes and scores, and determines if the class for a person (1) is available within a certain score and within a certain amount of objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def object_counter(classes_arr, scores_arr, score_thresh=0.5):\n",
    "    # Process the numpy array of classes from the model\n",
    "    stacked_arr = np.stack((classes_arr, scores_arr), axis=-1)\n",
    "    # Convert to pandas dataframe for easier querying\n",
    "    detection_df = pd.DataFrame(stacked_arr)\n",
    "    # Retrieve total count of cars with score threshold above param value\n",
    "    detected_cars = detection_df[(detection_df[0] == 3.0) & (detection_df[1] > score_thresh)]\n",
    "    detected_people =  detection_df[(detection_df[0] == 1.0) & (detection_df[1] > score_thresh)]\n",
    "    detected_bicycles =  detection_df[(detection_df[0] == 2.0) & (detection_df[1] > score_thresh)]\n",
    "    detected_motorcycles =  detection_df[(detection_df[0] == 4.0) & (detection_df[1] > score_thresh)]\n",
    "    detected_buses =  detection_df[(detection_df[0] == 6.0) & (detection_df[1] > score_thresh)]\n",
    "    detected_trucks =  detection_df[(detection_df[0] == 8.0) & (detection_df[1] > score_thresh)]\n",
    "    \n",
    "    car_count = len(detected_cars)\n",
    "    people_count = len(detected_people)\n",
    "    bicycle_count = len(detected_bicycles)\n",
    "    motorcycle_count = len(detected_motorcycles)\n",
    "    bus_count = len(detected_buses)\n",
    "    truck_count = len(detected_trucks)\n",
    "\n",
    "    return car_count, people_count, bicycle_count, motorcycle_count, bus_count, truck_count\n",
    "\n",
    "def return_lat(row):\n",
    "    return row['location']['latitude']\n",
    "\n",
    "def return_lon(row):\n",
    "    return row['location']['longitude']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network Camera Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  import sys\n"
     ]
    }
   ],
   "source": [
    "# TrafficLand API source JSON\n",
    "source_json = r\"http://api.trafficland.com/v1.5/json/video_feeds?system=ddot&key=1594c8892d7fbd18181a8a6a44958b28\"\n",
    "df = pd.read_json(source_json)\n",
    "ddot_df = df.loc[df.provider == 'DDOT']\n",
    "# Edit the ddot_df to make a feature service using the API\n",
    "ddot_df['latitude'] = ddot_df.apply(lambda row: return_lat(row), axis=1)\n",
    "ddot_df['longitude'] = ddot_df.apply(lambda row: return_lon(row), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Camera Iteration Prototyping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Size, in inches, of the output images.\n",
    "IMAGE_SIZE = (12, 8)\n",
    "jpeg_size = \"halfJpeg\" # Other Options: halfJpeg; fullJpeg; hugeJpeg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P1: Use existing feature service and send model outputs as updates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RT Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=cbe1f0e312994a21ae16ad8f841caf8d' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=cbe1f0e312994a21ae16ad8f841caf8d' target='_blank'><b>DDOT Traffic Cameras (Firefly)</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://esrifederal.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by albe9057@esri.com_esrifederal\n",
       "                        <br/>Last Modified: March 05, 2018\n",
       "                        <br/>0 comments, 87 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"DDOT Traffic Cameras (Firefly)\" type:Feature Layer Collection owner:albe9057@esri.com_esrifederal>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_point_srvc = gis.content.search(\"DDOT Traffic Cameras\", item_type=\"feature service\")[1]\n",
    "object_point_srvc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stable Service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# object_point_srvc = gis.content.search(\"DDOT_TrafficCameras_CityNetwork\", item_type=\"feature service\")[0]\n",
    "# object_point_srvc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert our existing service into a pandas dataframe\n",
    "object_point_lyr = object_point_srvc.layers[0]\n",
    "obj_fset = object_point_lyr.query()  #querying without any conditions returns all the features\n",
    "obj_df = obj_fset.df\n",
    "all_features = obj_fset.features\n",
    "original_feature = all_features[1]\n",
    "feature_to_be_updated = deepcopy(original_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reset Routine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://esrifederal.maps.arcgis.com/home/webmap/viewer.html?webmap=255196da558749209dc63c07ffb38ae6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restting values for camera 0 of 100...\n",
      "Restting values for camera 1 of 100...\n",
      "Restting values for camera 2 of 100...\n",
      "Restting values for camera 3 of 100...\n",
      "Restting values for camera 4 of 100...\n",
      "Restting values for camera 5 of 100...\n",
      "Restting values for camera 6 of 100...\n",
      "Restting values for camera 7 of 100...\n",
      "Restting values for camera 8 of 100...\n",
      "Restting values for camera 9 of 100...\n",
      "Restting values for camera 10 of 100...\n",
      "Restting values for camera 11 of 100...\n",
      "Restting values for camera 12 of 100...\n",
      "Restting values for camera 13 of 100...\n",
      "Restting values for camera 14 of 100...\n",
      "Restting values for camera 15 of 100...\n",
      "Restting values for camera 16 of 100...\n",
      "Restting values for camera 17 of 100...\n",
      "Restting values for camera 18 of 100...\n",
      "Restting values for camera 19 of 100...\n",
      "Restting values for camera 20 of 100...\n",
      "Restting values for camera 21 of 100...\n",
      "Restting values for camera 22 of 100...\n",
      "Restting values for camera 23 of 100...\n",
      "Restting values for camera 24 of 100...\n",
      "Restting values for camera 25 of 100...\n",
      "Restting values for camera 26 of 100...\n",
      "Restting values for camera 27 of 100...\n",
      "Restting values for camera 28 of 100...\n",
      "Restting values for camera 29 of 100...\n",
      "Restting values for camera 30 of 100...\n",
      "Restting values for camera 31 of 100...\n",
      "Restting values for camera 32 of 100...\n",
      "Restting values for camera 33 of 100...\n",
      "Restting values for camera 34 of 100...\n",
      "Restting values for camera 35 of 100...\n",
      "Restting values for camera 36 of 100...\n",
      "Restting values for camera 37 of 100...\n",
      "Restting values for camera 38 of 100...\n",
      "Restting values for camera 39 of 100...\n",
      "Restting values for camera 40 of 100...\n",
      "Restting values for camera 41 of 100...\n",
      "Restting values for camera 42 of 100...\n",
      "Restting values for camera 43 of 100...\n",
      "Restting values for camera 44 of 100...\n",
      "Restting values for camera 45 of 100...\n",
      "Restting values for camera 46 of 100...\n",
      "Restting values for camera 47 of 100...\n",
      "Restting values for camera 48 of 100...\n",
      "Restting values for camera 49 of 100...\n",
      "Restting values for camera 50 of 100...\n",
      "Restting values for camera 51 of 100...\n",
      "Restting values for camera 52 of 100...\n",
      "Restting values for camera 53 of 100...\n",
      "Restting values for camera 54 of 100...\n",
      "Restting values for camera 55 of 100...\n",
      "Restting values for camera 56 of 100...\n",
      "Restting values for camera 57 of 100...\n",
      "Restting values for camera 58 of 100...\n",
      "Restting values for camera 59 of 100...\n",
      "Restting values for camera 60 of 100...\n",
      "Restting values for camera 61 of 100...\n",
      "Restting values for camera 62 of 100...\n",
      "Restting values for camera 63 of 100...\n",
      "Restting values for camera 64 of 100...\n",
      "Restting values for camera 65 of 100...\n",
      "Restting values for camera 66 of 100...\n",
      "Restting values for camera 67 of 100...\n",
      "Restting values for camera 68 of 100...\n",
      "Restting values for camera 69 of 100...\n",
      "Restting values for camera 70 of 100...\n",
      "Restting values for camera 71 of 100...\n",
      "Restting values for camera 72 of 100...\n",
      "Restting values for camera 73 of 100...\n",
      "Restting values for camera 74 of 100...\n",
      "Restting values for camera 75 of 100...\n",
      "Restting values for camera 76 of 100...\n",
      "Restting values for camera 77 of 100...\n",
      "Restting values for camera 78 of 100...\n",
      "Restting values for camera 79 of 100...\n",
      "Restting values for camera 80 of 100...\n",
      "Restting values for camera 81 of 100...\n",
      "Restting values for camera 82 of 100...\n",
      "Restting values for camera 83 of 100...\n",
      "Restting values for camera 84 of 100...\n",
      "Restting values for camera 85 of 100...\n",
      "Restting values for camera 86 of 100...\n",
      "Restting values for camera 87 of 100...\n",
      "Restting values for camera 88 of 100...\n",
      "Restting values for camera 89 of 100...\n",
      "Restting values for camera 90 of 100...\n",
      "Restting values for camera 91 of 100...\n",
      "Restting values for camera 92 of 100...\n",
      "Restting values for camera 93 of 100...\n",
      "Restting values for camera 94 of 100...\n",
      "Restting values for camera 95 of 100...\n",
      "Restting values for camera 96 of 100...\n",
      "Restting values for camera 97 of 100...\n",
      "Restting values for camera 98 of 100...\n",
      "Restting values for camera 99 of 100...\n",
      "Restting values for camera 100 of 100...\n",
      "Restting values for camera 101 of 100...\n",
      "Restting values for camera 102 of 100...\n",
      "Restting values for camera 103 of 100...\n",
      "Restting values for camera 104 of 100...\n",
      "Restting values for camera 105 of 100...\n",
      "Restting values for camera 106 of 100...\n",
      "Restting values for camera 107 of 100...\n",
      "Restting values for camera 108 of 100...\n",
      "Restting values for camera 109 of 100...\n",
      "Restting values for camera 110 of 100...\n"
     ]
    }
   ],
   "source": [
    "for iteration in range(1):\n",
    "    \n",
    "    features_for_update = []\n",
    "            \n",
    "    for ix in range(0,111):\n",
    "        print(\"Resetting values for camera {0} of 100...\".format(str(ix)))\n",
    "\n",
    "        # Retrieve camera information\n",
    "        camera_name = ddot_df.iloc[ix]['name']\n",
    "\n",
    "        # Retrieve the feature from the feature layer to update\n",
    "        obj_fset = object_point_lyr.query(where=\"\"\"name = '\"\"\"+camera_name+\"\"\"'\"\"\", return_geometry=False)  \n",
    "        all_features = obj_fset.features\n",
    "        original_feature = all_features[0]\n",
    "        feature_to_be_updated = deepcopy(original_feature)\n",
    "\n",
    "        \n",
    "        feature_to_be_updated.attributes['rt_object_count'] = 0\n",
    "        feature_to_be_updated.attributes['rt_vehicle_count'] = 0\n",
    "        feature_to_be_updated.attributes['rt_car_count'] = 0\n",
    "        feature_to_be_updated.attributes['rt_bus_count'] = 0\n",
    "        feature_to_be_updated.attributes['rt_truck_count'] = 0\n",
    "        feature_to_be_updated.attributes['rt_motorcycle_count'] = 0\n",
    "        feature_to_be_updated.attributes['rt_pedestrian_count'] = 0\n",
    "        feature_to_be_updated.attributes['rt_bicycle_count'] = 0\n",
    "        \n",
    "        feature_to_be_updated.attributes['histval_10am_th_car'] = None\n",
    "        feature_to_be_updated.attributes['histval_10am_th_ped'] = None\n",
    "        feature_to_be_updated.attributes['histval_10am_th_bus'] = None\n",
    "        feature_to_be_updated.attributes['histval_10am_th_trck'] = None\n",
    "        \n",
    "        feature_to_be_updated.attributes['thrsh_status_cars'] = \"Not Tracked\"\n",
    "        feature_to_be_updated.attributes['thrsh_status_ped'] = \"Not Tracked\" \n",
    "        feature_to_be_updated.attributes['thrsh_status_bus'] = \"Not Tracked\" \n",
    "        feature_to_be_updated.attributes['thrsh_status_trck'] = \"Not Tracked\"           \n",
    "\n",
    "        features_for_update.append(feature_to_be_updated)\n",
    "        \n",
    "    object_point_lyr.edit_features(updates=features_for_update)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set trend threshold percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting trend threshold values for camera 0 of 111...\n",
      "Setting trend threshold values for camera 1 of 111...\n",
      "Setting trend threshold values for camera 2 of 111...\n",
      "Setting trend threshold values for camera 3 of 111...\n",
      "Setting trend threshold values for camera 4 of 111...\n",
      "Setting trend threshold values for camera 5 of 111...\n",
      "Setting trend threshold values for camera 6 of 111...\n",
      "Setting trend threshold values for camera 7 of 111...\n",
      "Setting trend threshold values for camera 8 of 111...\n",
      "Setting trend threshold values for camera 9 of 111...\n",
      "Setting trend threshold values for camera 10 of 111...\n",
      "Setting trend threshold values for camera 11 of 111...\n",
      "Setting trend threshold values for camera 12 of 111...\n",
      "Setting trend threshold values for camera 13 of 111...\n",
      "Setting trend threshold values for camera 14 of 111...\n",
      "Setting trend threshold values for camera 15 of 111...\n",
      "Setting trend threshold values for camera 16 of 111...\n",
      "Setting trend threshold values for camera 17 of 111...\n",
      "Setting trend threshold values for camera 18 of 111...\n",
      "Setting trend threshold values for camera 19 of 111...\n",
      "Setting trend threshold values for camera 20 of 111...\n",
      "Setting trend threshold values for camera 21 of 111...\n",
      "Setting trend threshold values for camera 22 of 111...\n",
      "Setting trend threshold values for camera 23 of 111...\n",
      "Setting trend threshold values for camera 24 of 111...\n",
      "Setting trend threshold values for camera 25 of 111...\n",
      "Setting trend threshold values for camera 26 of 111...\n",
      "Setting trend threshold values for camera 27 of 111...\n",
      "Setting trend threshold values for camera 28 of 111...\n",
      "Setting trend threshold values for camera 29 of 111...\n",
      "Setting trend threshold values for camera 30 of 111...\n",
      "Setting trend threshold values for camera 31 of 111...\n",
      "Setting trend threshold values for camera 32 of 111...\n",
      "Setting trend threshold values for camera 33 of 111...\n",
      "Setting trend threshold values for camera 34 of 111...\n",
      "Setting trend threshold values for camera 35 of 111...\n",
      "Setting trend threshold values for camera 36 of 111...\n",
      "Setting trend threshold values for camera 37 of 111...\n",
      "Setting trend threshold values for camera 38 of 111...\n",
      "Setting trend threshold values for camera 39 of 111...\n",
      "Setting trend threshold values for camera 40 of 111...\n",
      "Setting trend threshold values for camera 41 of 111...\n",
      "Setting trend threshold values for camera 42 of 111...\n",
      "Setting trend threshold values for camera 43 of 111...\n",
      "Setting trend threshold values for camera 44 of 111...\n",
      "Setting trend threshold values for camera 45 of 111...\n",
      "Setting trend threshold values for camera 46 of 111...\n",
      "Setting trend threshold values for camera 47 of 111...\n",
      "Setting trend threshold values for camera 48 of 111...\n",
      "Setting trend threshold values for camera 49 of 111...\n",
      "Setting trend threshold values for camera 50 of 111...\n",
      "Setting trend threshold values for camera 51 of 111...\n",
      "Setting trend threshold values for camera 52 of 111...\n",
      "Setting trend threshold values for camera 53 of 111...\n",
      "Setting trend threshold values for camera 54 of 111...\n",
      "Setting trend threshold values for camera 55 of 111...\n",
      "Setting trend threshold values for camera 56 of 111...\n",
      "Setting trend threshold values for camera 57 of 111...\n",
      "Setting trend threshold values for camera 58 of 111...\n",
      "Setting trend threshold values for camera 59 of 111...\n",
      "Setting trend threshold values for camera 60 of 111...\n",
      "Setting trend threshold values for camera 61 of 111...\n",
      "Setting trend threshold values for camera 62 of 111...\n",
      "Setting trend threshold values for camera 63 of 111...\n",
      "Setting trend threshold values for camera 64 of 111...\n",
      "Setting trend threshold values for camera 65 of 111...\n",
      "Setting trend threshold values for camera 66 of 111...\n",
      "Setting trend threshold values for camera 67 of 111...\n",
      "Setting trend threshold values for camera 68 of 111...\n",
      "Setting trend threshold values for camera 69 of 111...\n",
      "Setting trend threshold values for camera 70 of 111...\n",
      "Setting trend threshold values for camera 71 of 111...\n",
      "Setting trend threshold values for camera 72 of 111...\n",
      "Setting trend threshold values for camera 73 of 111...\n",
      "Setting trend threshold values for camera 74 of 111...\n",
      "Setting trend threshold values for camera 75 of 111...\n",
      "Setting trend threshold values for camera 76 of 111...\n",
      "Setting trend threshold values for camera 77 of 111...\n",
      "Setting trend threshold values for camera 78 of 111...\n",
      "Setting trend threshold values for camera 79 of 111...\n",
      "Setting trend threshold values for camera 80 of 111...\n",
      "Setting trend threshold values for camera 81 of 111...\n",
      "Setting trend threshold values for camera 82 of 111...\n",
      "Setting trend threshold values for camera 83 of 111...\n",
      "Setting trend threshold values for camera 84 of 111...\n",
      "Setting trend threshold values for camera 85 of 111...\n",
      "Setting trend threshold values for camera 86 of 111...\n",
      "Setting trend threshold values for camera 87 of 111...\n",
      "Setting trend threshold values for camera 88 of 111...\n",
      "Setting trend threshold values for camera 89 of 111...\n",
      "Setting trend threshold values for camera 90 of 111...\n",
      "Setting trend threshold values for camera 91 of 111...\n",
      "Setting trend threshold values for camera 92 of 111...\n",
      "Setting trend threshold values for camera 93 of 111...\n",
      "Setting trend threshold values for camera 94 of 111...\n",
      "Setting trend threshold values for camera 95 of 111...\n",
      "Setting trend threshold values for camera 96 of 111...\n",
      "Setting trend threshold values for camera 97 of 111...\n",
      "Setting trend threshold values for camera 98 of 111...\n",
      "Setting trend threshold values for camera 99 of 111...\n",
      "Setting trend threshold values for camera 100 of 111...\n",
      "Setting trend threshold values for camera 101 of 111...\n",
      "Setting trend threshold values for camera 102 of 111...\n",
      "Setting trend threshold values for camera 103 of 111...\n",
      "Setting trend threshold values for camera 104 of 111...\n",
      "Setting trend threshold values for camera 105 of 111...\n",
      "Setting trend threshold values for camera 106 of 111...\n",
      "Setting trend threshold values for camera 107 of 111...\n",
      "Setting trend threshold values for camera 108 of 111...\n",
      "Setting trend threshold values for camera 109 of 111...\n",
      "Setting trend threshold values for camera 110 of 111...\n"
     ]
    }
   ],
   "source": [
    "thresh_percent = 2.0\n",
    "\n",
    "reset_trends = True\n",
    "\n",
    "for iteration in range(1):\n",
    "    \n",
    "    features_for_update = []\n",
    "            \n",
    "    for ix in range(0,111):\n",
    "        print(\"Setting trend threshold values for camera {0} of 111...\".format(str(ix)))\n",
    "\n",
    "        # Retrieve camera information\n",
    "        camera_name = ddot_df.iloc[ix]['name']\n",
    "\n",
    "        # Retrieve the feature from the feature layer to update\n",
    "        obj_fset = object_point_lyr.query(where=\"\"\"name = '\"\"\"+camera_name+\"\"\"'\"\"\", return_geometry=False)  \n",
    "        all_features = obj_fset.features\n",
    "        original_feature = all_features[0]\n",
    "        feature_to_be_updated = deepcopy(original_feature)\n",
    "        \n",
    "        feature_to_be_updated.attributes['threshold_val_cars'] = thresh_percent\n",
    "        feature_to_be_updated.attributes['threshold_val_ped'] = thresh_percent\n",
    "        feature_to_be_updated.attributes['threshold_val_buses'] = thresh_percent\n",
    "        feature_to_be_updated.attributes['threshold_val_trck'] = thresh_percent    \n",
    "        \n",
    "        feature_to_be_updated.attributes['thrsh_status_cars'] = \"Not Tracked\"\n",
    "        feature_to_be_updated.attributes['thrsh_status_ped'] = \"Not Tracked\" \n",
    "        feature_to_be_updated.attributes['thrsh_status_bus'] = \"Not Tracked\" \n",
    "        feature_to_be_updated.attributes['thrsh_status_trck'] = \"Not Tracked\" \n",
    "        \n",
    "        if reset_trends:\n",
    "            feature_to_be_updated.attributes['histval_10am_th_car'] = None\n",
    "            feature_to_be_updated.attributes['histval_10am_th_ped'] = None\n",
    "            feature_to_be_updated.attributes['histval_10am_th_bus'] = None\n",
    "            feature_to_be_updated.attributes['histval_10am_th_trck'] = None\n",
    "\n",
    "\n",
    "        features_for_update.append(feature_to_be_updated)\n",
    "        \n",
    "    object_point_lyr.edit_features(updates=features_for_update)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Update with Attachments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "overwrite_attachment = True\n",
    "\n",
    "upload_source = False\n",
    "\n",
    "with detection_graph.as_default():\n",
    "    \n",
    "    with tf.Session(graph=detection_graph) as sess:\n",
    "        \n",
    "        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
    "        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
    "        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
    "        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
    "        num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
    "\n",
    "        for iteration in range(10):\n",
    "            # TrafficLand API source JSON\n",
    "            source_json = r\"http://api.trafficland.com/v1.5/json/video_feeds?system=ddot&key=1594c8892d7fbd18181a8a6a44958b28\"\n",
    "            df = pd.read_json(source_json)\n",
    "            ddot_df = df.loc[df.provider == 'DDOT']\n",
    "            \n",
    "            for ix in range(1,110):\n",
    "                print(\"Processing camera {0} of 110...\".format(str(ix)))\n",
    "\n",
    "                # Retrieve camera information\n",
    "                camera_jpeg_url = ddot_df.iloc[ix]['content']['hugeJpeg']\n",
    "                camera_name = ddot_df.iloc[ix]['name']\n",
    "                camera_id = ddot_df.iloc[ix]['publicId']\n",
    "                \n",
    "\n",
    "                # Retrieve the camera image\n",
    "                camera_response = requests.get(camera_jpeg_url)\n",
    "                \n",
    "#                 # Perform attachment of source image\n",
    "                if upload_source:\n",
    "                    streamed_response = requests.get(camera_jpeg_url, stream=True)\n",
    "                    with open('source.jpg', 'wb') as out_file:\n",
    "                        shutil.copyfileobj(streamed_response.raw, out_file)\n",
    "    #                 del response                \n",
    "\n",
    "                image = Image.open(BytesIO(camera_response.content))\n",
    "                image_np = load_image_into_numpy_array(image)\n",
    "                image_np_expanded = np.expand_dims(image_np, axis=0)\n",
    "\n",
    "                (boxes, scores, classes, num) = sess.run(\n",
    "                      [detection_boxes, detection_scores, detection_classes, num_detections],\n",
    "                      feed_dict={image_tensor: image_np_expanded})\n",
    "\n",
    "                vis_util.visualize_boxes_and_labels_on_image_array(\n",
    "                      image_np,\n",
    "                      np.squeeze(boxes),\n",
    "                      np.squeeze(classes).astype(np.int32),\n",
    "                      np.squeeze(scores),\n",
    "                      category_index,\n",
    "                      use_normalized_coordinates=True,\n",
    "                      line_thickness=8)\n",
    "                \n",
    "                car_count, people_count, bicycle_count, motorcycle_count, bus_count, truck_count = object_counter(np.squeeze(classes).astype(np.int32), np.squeeze(scores))\n",
    "                vehicle_count = car_count + motorcycle_count + bus_count + truck_count\n",
    "                total_count = vehicle_count + bicycle_count + people_count\n",
    "                \n",
    "                # Retrieve the feature from the feature layer to update\n",
    "                obj_fset = object_point_lyr.query(where=\"\"\"name = '\"\"\"+camera_name+\"\"\"'\"\"\", return_geometry=False)  \n",
    "                all_features = obj_fset.features\n",
    "                original_feature = all_features[0]\n",
    "                feature_to_be_updated = deepcopy(original_feature)\n",
    "                \n",
    "                features_for_update = []\n",
    "                feature_to_be_updated.attributes['rt_object_count'] = total_count\n",
    "                feature_to_be_updated.attributes['rt_vehicle_count'] = vehicle_count\n",
    "                feature_to_be_updated.attributes['rt_car_count'] = car_count\n",
    "                feature_to_be_updated.attributes['rt_bus_count'] = bus_count\n",
    "                feature_to_be_updated.attributes['rt_truck_count'] = truck_count\n",
    "                feature_to_be_updated.attributes['rt_motorcycle_count'] = motorcycle_count\n",
    "                feature_to_be_updated.attributes['rt_pedestrian_count'] = people_count\n",
    "                feature_to_be_updated.attributes['rt_bicycle_count'] = bicycle_count\n",
    "                \n",
    "                feature_to_be_updated.attributes['source_image'] = camera_jpeg_url\n",
    "\n",
    "    #             feature_to_be_updated.attributes['rt_car_count'] = car_count\n",
    "                features_for_update.append(feature_to_be_updated)\n",
    "                object_point_lyr.edit_features(updates=features_for_update)    \n",
    "        \n",
    "                # Perform attachment of detected image\n",
    "                scipy.misc.imsave('detected_objs.jpg', image_np)\n",
    "                obj_id = feature_to_be_updated.attributes['F__OBJECTID']\n",
    "                \n",
    "                if overwrite_attachment:\n",
    "                    for attachment in object_point_lyr.attachments.get_list(obj_id):\n",
    "                        object_point_lyr.attachments.delete(obj_id, attachment['id'])\n",
    "                                               \n",
    "                object_point_lyr.attachments.add(obj_id, 'detected_objs.jpg')\n",
    "                \n",
    "                if upload_source:\n",
    "                    object_point_lyr.attachments.add(obj_id, 'source.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Update with Attachments and Trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "overwrite_attachment = True\n",
    "\n",
    "upload_source = False\n",
    "\n",
    "write_trends = True\n",
    "\n",
    "reset_trends = True\n",
    "\n",
    "with detection_graph.as_default():\n",
    "    \n",
    "    with tf.Session(graph=detection_graph) as sess:\n",
    "        \n",
    "        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
    "        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
    "        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
    "        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
    "        num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
    "\n",
    "        for iteration in range(10):\n",
    "            # TrafficLand API source JSON\n",
    "            source_json = r\"http://api.trafficland.com/v1.5/json/video_feeds?system=ddot&key=1594c8892d7fbd18181a8a6a44958b28\"\n",
    "            df = pd.read_json(source_json)\n",
    "            ddot_df = df.loc[df.provider == 'DDOT']\n",
    "            \n",
    "            for ix in range(1,110):\n",
    "                print(\"Processing camera {0} of 110...\".format(str(ix)))\n",
    "\n",
    "                # Retrieve camera information\n",
    "                camera_jpeg_url = ddot_df.iloc[ix]['content']['hugeJpeg']\n",
    "                camera_name = ddot_df.iloc[ix]['name']\n",
    "                camera_id = ddot_df.iloc[ix]['publicId']\n",
    "                \n",
    "                # Retrieve the camera image\n",
    "                camera_response = requests.get(camera_jpeg_url)\n",
    "                \n",
    "#                 # Perform attachment of source image\n",
    "                if upload_source:\n",
    "                    streamed_response = requests.get(camera_jpeg_url, stream=True)\n",
    "                    with open('source.jpg', 'wb') as out_file:\n",
    "                        shutil.copyfileobj(streamed_response.raw, out_file)\n",
    "    #                 del response                \n",
    "\n",
    "                image = Image.open(BytesIO(camera_response.content))\n",
    "                image_np = load_image_into_numpy_array(image)\n",
    "                image_np_expanded = np.expand_dims(image_np, axis=0)\n",
    "\n",
    "                (boxes, scores, classes, num) = sess.run(\n",
    "                      [detection_boxes, detection_scores, detection_classes, num_detections],\n",
    "                      feed_dict={image_tensor: image_np_expanded})\n",
    "\n",
    "                vis_util.visualize_boxes_and_labels_on_image_array(\n",
    "                      image_np,\n",
    "                      np.squeeze(boxes),\n",
    "                      np.squeeze(classes).astype(np.int32),\n",
    "                      np.squeeze(scores),\n",
    "                      category_index,\n",
    "                      use_normalized_coordinates=True,\n",
    "                      line_thickness=8)\n",
    "                \n",
    "                car_count, people_count, bicycle_count, motorcycle_count, bus_count, truck_count = object_counter(np.squeeze(classes).astype(np.int32), np.squeeze(scores))\n",
    "                vehicle_count = car_count + motorcycle_count + bus_count + truck_count\n",
    "                total_count = vehicle_count + bicycle_count + people_count\n",
    "                \n",
    "                # Retrieve the feature from the feature layer to update\n",
    "                obj_fset = object_point_lyr.query(where=\"\"\"name = '\"\"\"+camera_name+\"\"\"'\"\"\", return_geometry=False)  \n",
    "                all_features = obj_fset.features\n",
    "                original_feature = all_features[0]\n",
    "                feature_to_be_updated = deepcopy(original_feature)\n",
    "                \n",
    "                features_for_update = []\n",
    "                feature_to_be_updated.attributes['rt_object_count'] = total_count\n",
    "                feature_to_be_updated.attributes['rt_vehicle_count'] = vehicle_count\n",
    "                feature_to_be_updated.attributes['rt_car_count'] = car_count\n",
    "                feature_to_be_updated.attributes['rt_bus_count'] = bus_count\n",
    "                feature_to_be_updated.attributes['rt_truck_count'] = truck_count\n",
    "                feature_to_be_updated.attributes['rt_motorcycle_count'] = motorcycle_count\n",
    "                feature_to_be_updated.attributes['rt_pedestrian_count'] = people_count\n",
    "                feature_to_be_updated.attributes['rt_bicycle_count'] = bicycle_count\n",
    "                \n",
    "                feature_to_be_updated.attributes['source_image'] = camera_jpeg_url\n",
    "                \n",
    "                # Perform Trend Process\n",
    "                if write_trends:\n",
    "                    \n",
    "                    # If the trend needs to be reset\n",
    "                    if reset_trends and iteration==0:\n",
    "                        print(\"Resetting trends...\")\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_car'] = car_count\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_ped'] = people_count\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_bus'] = bus_count\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_trck'] = truck_count\n",
    "                        \n",
    "                        histval_car = car_count\n",
    "                        histval_ped = people_count\n",
    "                        histval_bus = bus_count\n",
    "                        histval_truck = truck_count\n",
    "\n",
    "                    # If the trend does not need to be reset\n",
    "                    else:\n",
    "                        histval_car = feature_to_be_updated.attributes['histval_10am_th_car']\n",
    "                        histval_ped = feature_to_be_updated.attributes['histval_10am_th_ped']\n",
    "                        histval_bus = feature_to_be_updated.attributes['histval_10am_th_bus']\n",
    "                        histval_truck = feature_to_be_updated.attributes['histval_10am_th_trck']\n",
    "                        \n",
    "                        if histval_car == None:\n",
    "                            histval_car = car_count\n",
    "                        if histval_bus == None:\n",
    "                            histval_bus = bus_count\n",
    "                        if histval_truck == None:\n",
    "                            histval_truck = truck_count\n",
    "                        if histval_ped == None:\n",
    "                            histval_ped = people_count                            \n",
    "                    \n",
    "                        # Calculate the new trend value and store it for the new update\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_car'] = (car_count + histval_car) / 2\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_ped'] = (people_count + histval_ped) / 2\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_bus'] = (bus_count + histval_bus) / 2\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_trck'] = (truck_count + histval_truck) / 2\n",
    "                        \n",
    "                    # Determine threshold percent, value, and status\n",
    "                    thrshpcnt_car = feature_to_be_updated.attributes['threshold_val_cars']\n",
    "                    thrshpcnt_ped = feature_to_be_updated.attributes['threshold_val_ped']\n",
    "                    thrshpcnt_bus = feature_to_be_updated.attributes['threshold_val_buses']\n",
    "                    thrshpcnt_truck = feature_to_be_updated.attributes['threshold_val_trck']\n",
    "                    \n",
    "                    # Perform writes to the threshold status field\n",
    "                    \n",
    "                    if car_count > (histval_car * thrshpcnt_car):\n",
    "                        feature_to_be_updated.attributes['thrsh_status_cars'] = \"Above\"           \n",
    "                    else:\n",
    "                        feature_to_be_updated.attributes['thrsh_status_cars'] = \"Below\"\n",
    "                        \n",
    "                    if people_count > (histval_ped * thrshpcnt_ped):\n",
    "                        feature_to_be_updated.attributes['thrsh_status_ped'] = \"Above\"\n",
    "                    else:\n",
    "                        feature_to_be_updated.attributes['thrsh_status_ped'] = \"Below\"                        \n",
    "                        \n",
    "                    if bus_count > (histval_bus * thrshpcnt_bus):\n",
    "                        feature_to_be_updated.attributes['thrsh_status_bus'] = \"Above\"\n",
    "                    else:\n",
    "                        feature_to_be_updated.attributes['thrsh_status_bus'] = \"Below\"                        \n",
    "                        \n",
    "                    if truck_count > (histval_truck * thrshpcnt_truck):\n",
    "                        feature_to_be_updated.attributes['thrsh_status_trck'] = \"Above\"\n",
    "                    else:\n",
    "                        feature_to_be_updated.attributes['thrsh_status_trck'] = \"Below\"                        \n",
    "                                               \n",
    "                # Store attribute updates and send edit request\n",
    "                features_for_update.append(feature_to_be_updated)\n",
    "                object_point_lyr.edit_features(updates=features_for_update)    \n",
    "        \n",
    "                # Perform attachment of detected image\n",
    "                scipy.misc.imsave('detected_objs.jpg', image_np)\n",
    "                obj_id = feature_to_be_updated.attributes['F__OBJECTID']\n",
    "                \n",
    "                if overwrite_attachment:\n",
    "                    for attachment in object_point_lyr.attachments.get_list(obj_id):\n",
    "                        object_point_lyr.attachments.delete(obj_id, attachment['id'])\n",
    "                                               \n",
    "                object_point_lyr.attachments.add(obj_id, 'detected_objs.jpg')\n",
    "                \n",
    "                if upload_source:\n",
    "                    object_point_lyr.attachments.add(obj_id, 'source.jpg')\n",
    "                                    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform Demo Updates (Model, Attachments, Trends) Against Constitution Ave Event Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set thresh percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting thresh percent values for camera '9th St @ Constitution Ave'...\n",
      "Setting thresh percent values for camera '12th St @ Constitution Ave'...\n",
      "Setting thresh percent values for camera '14th St @ Pennsylvania Ave'...\n",
      "Setting thresh percent values for camera 'Pennsylvania Ave @ 7th St'...\n",
      "Setting thresh percent values for camera 'Pennsylvania Ave @ 9th St'...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'addResults': [],\n",
       " 'deleteResults': [],\n",
       " 'updateResults': [{'globalId': '5c8098bb-7801-4d8a-9731-bd7768773e0a',\n",
       "   'objectId': 57,\n",
       "   'success': True},\n",
       "  {'globalId': '40702ac6-06f3-48a5-aa00-90567436c241',\n",
       "   'objectId': 56,\n",
       "   'success': True},\n",
       "  {'globalId': '4236302a-9c65-4e01-b6fd-cf0a6a765b37',\n",
       "   'objectId': 50,\n",
       "   'success': True},\n",
       "  {'globalId': 'bed5f585-2576-4206-b904-05099946434e',\n",
       "   'objectId': 25,\n",
       "   'success': True},\n",
       "  {'globalId': '9fdc4448-186f-4dfc-92c0-2086a25026a0',\n",
       "   'objectId': 73,\n",
       "   'success': True}]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_feature_names = [\n",
    "    \"9th St @ Constitution Ave\",\n",
    "    \"12th St @ Constitution Ave\",\n",
    "    \"14th St @ Pennsylvania Ave\",\n",
    "    \"Pennsylvania Ave @ 7th St\",\n",
    "    \"Pennsylvania Ave @ 9th St\"\n",
    "]\n",
    "\n",
    "thresh_percent = 1.125\n",
    "\n",
    "features_for_update = []\n",
    "\n",
    "for event_feature_name in event_feature_names:\n",
    "    print(\"Setting thresh percent values for camera '{0}'...\".format(str(event_feature_name)))\n",
    "\n",
    "    record = ddot_df.loc[ddot_df.name == event_feature_name]\n",
    "\n",
    "    # Retrieve camera information\n",
    "    camera_name = record['name'].all()\n",
    "\n",
    "    # Retrieve the feature from the feature layer to update\n",
    "    obj_fset = object_point_lyr.query(where=\"\"\"name = '\"\"\"+camera_name+\"\"\"'\"\"\", return_geometry=False)  \n",
    "    all_features = obj_fset.features\n",
    "    original_feature = all_features[0]\n",
    "    feature_to_be_updated = deepcopy(original_feature)\n",
    "\n",
    "    feature_to_be_updated.attributes['threshold_val_cars'] = thresh_percent\n",
    "    feature_to_be_updated.attributes['threshold_val_ped'] = thresh_percent\n",
    "    feature_to_be_updated.attributes['threshold_val_buses'] = thresh_percent\n",
    "    feature_to_be_updated.attributes['threshold_val_trck'] = thresh_percent\n",
    "    \n",
    "    features_for_update.append(feature_to_be_updated)\n",
    "\n",
    "object_point_lyr.edit_features(updates=features_for_update)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reset Trend Values for Event Locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resetting values for camera '9th St @ Constitution Ave'...\n",
      "Resetting values for camera '12th St @ Constitution Ave'...\n",
      "Resetting values for camera '14th St @ Pennsylvania Ave'...\n",
      "Resetting values for camera 'Pennsylvania Ave @ 7th St'...\n",
      "Resetting values for camera 'Pennsylvania Ave @ 9th St'...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'addResults': [],\n",
       " 'deleteResults': [],\n",
       " 'updateResults': [{'globalId': '5c8098bb-7801-4d8a-9731-bd7768773e0a',\n",
       "   'objectId': 57,\n",
       "   'success': True},\n",
       "  {'globalId': '40702ac6-06f3-48a5-aa00-90567436c241',\n",
       "   'objectId': 56,\n",
       "   'success': True},\n",
       "  {'globalId': '4236302a-9c65-4e01-b6fd-cf0a6a765b37',\n",
       "   'objectId': 50,\n",
       "   'success': True},\n",
       "  {'globalId': 'bed5f585-2576-4206-b904-05099946434e',\n",
       "   'objectId': 25,\n",
       "   'success': True},\n",
       "  {'globalId': '9fdc4448-186f-4dfc-92c0-2086a25026a0',\n",
       "   'objectId': 73,\n",
       "   'success': True}]}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_feature_names = [\n",
    "    \"9th St @ Constitution Ave\",\n",
    "    \"12th St @ Constitution Ave\",\n",
    "    \"14th St @ Pennsylvania Ave\",\n",
    "    \"Pennsylvania Ave @ 7th St\",\n",
    "    \"Pennsylvania Ave @ 9th St\"\n",
    "]\n",
    "\n",
    "features_for_update = []\n",
    "\n",
    "for event_feature_name in event_feature_names:\n",
    "    print(\"Resetting values for camera '{0}'...\".format(str(event_feature_name)))\n",
    "\n",
    "    record = ddot_df.loc[ddot_df.name == event_feature_name]\n",
    "\n",
    "    # Retrieve camera information\n",
    "    camera_name = record['name'].all()\n",
    "\n",
    "    # Retrieve the feature from the feature layer to update\n",
    "    obj_fset = object_point_lyr.query(where=\"\"\"name = '\"\"\"+camera_name+\"\"\"'\"\"\", return_geometry=False)  \n",
    "    all_features = obj_fset.features\n",
    "    original_feature = all_features[0]\n",
    "    feature_to_be_updated = deepcopy(original_feature)\n",
    "\n",
    "\n",
    "    feature_to_be_updated.attributes['rt_object_count'] = 0\n",
    "    feature_to_be_updated.attributes['rt_vehicle_count'] = 0\n",
    "    feature_to_be_updated.attributes['rt_car_count'] = 0\n",
    "    feature_to_be_updated.attributes['rt_bus_count'] = 0\n",
    "    feature_to_be_updated.attributes['rt_truck_count'] = 0\n",
    "    feature_to_be_updated.attributes['rt_motorcycle_count'] = 0\n",
    "    feature_to_be_updated.attributes['rt_pedestrian_count'] = 0\n",
    "    feature_to_be_updated.attributes['rt_bicycle_count'] = 0\n",
    "\n",
    "    feature_to_be_updated.attributes['histval_10am_th_car'] = None\n",
    "    feature_to_be_updated.attributes['histval_10am_th_ped'] = None\n",
    "    feature_to_be_updated.attributes['histval_10am_th_bus'] = None\n",
    "    feature_to_be_updated.attributes['histval_10am_th_trck'] = None\n",
    "    \n",
    "    feature_to_be_updated.attributes['histval_10am_th_trck'] = None\n",
    "    \n",
    "    feature_to_be_updated.attributes['thrsh_status_cars'] = \"Not Tracked\"\n",
    "    feature_to_be_updated.attributes['thrsh_status_ped'] = \"Not Tracked\" \n",
    "    feature_to_be_updated.attributes['thrsh_status_bus'] = \"Not Tracked\" \n",
    "    feature_to_be_updated.attributes['thrsh_status_trck'] = \"Not Tracked\"      \n",
    "\n",
    "    features_for_update.append(feature_to_be_updated)\n",
    "\n",
    "object_point_lyr.edit_features(updates=features_for_update)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Demo Event Features Against Screencap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1920 x 1080"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Single Feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resetting trends...\n"
     ]
    }
   ],
   "source": [
    "event_feature_name = \"9th St @ Constitution Ave\"\n",
    "\n",
    "overwrite_attachment = True\n",
    "\n",
    "write_trends = True\n",
    "\n",
    "reset_trends = True\n",
    "\n",
    "iteration=0\n",
    "\n",
    "with detection_graph.as_default():\n",
    "    \n",
    "    with tf.Session(graph=detection_graph) as sess:\n",
    "        \n",
    "        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
    "        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
    "        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
    "        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
    "        num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
    "\n",
    "        while True: \n",
    "            \n",
    "            record = ddot_df.loc[ddot_df.name == event_feature_name]\n",
    "\n",
    "            # Retrieve camera information\n",
    "            camera_name = record['name'].all()\n",
    "\n",
    "            # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
    "            image_np = np.array(ImageGrab.grab(bbox=(0,510,960,1020)))\n",
    "            image_np_expanded = np.expand_dims(image_np, axis=0)\n",
    "\n",
    "            (boxes, scores, classes, num) = sess.run(\n",
    "                  [detection_boxes, detection_scores, detection_classes, num_detections],\n",
    "                  feed_dict={image_tensor: image_np_expanded})\n",
    "            \n",
    "            # Visualization of the results of a detection.\n",
    "            vis_util.visualize_boxes_and_labels_on_image_array(\n",
    "              image_np,\n",
    "              np.squeeze(boxes),\n",
    "              np.squeeze(classes).astype(np.int32),\n",
    "              np.squeeze(scores),\n",
    "              category_index,\n",
    "              use_normalized_coordinates=True,\n",
    "              line_thickness=8, min_score_thresh=0.5)\n",
    "            \n",
    "            cv2.imshow('object detection', cv2.resize(image_np, (960,510), interpolation=cv2.INTER_CUBIC))\n",
    "            if cv2.waitKey(25) & 0xFF == ord('q'):\n",
    "                cv2.destroyAllWindows()\n",
    "                break            \n",
    "\n",
    "            car_count, people_count, bicycle_count, motorcycle_count, bus_count, truck_count = object_counter(np.squeeze(classes).astype(np.int32), np.squeeze(scores))\n",
    "            vehicle_count = car_count + motorcycle_count + bus_count + truck_count\n",
    "            total_count = vehicle_count + bicycle_count + people_count\n",
    "\n",
    "            # Retrieve the feature from the feature layer to update\n",
    "            obj_fset = object_point_lyr.query(where=\"\"\"name = '\"\"\"+camera_name+\"\"\"'\"\"\", return_geometry=False)  \n",
    "            all_features = obj_fset.features\n",
    "            original_feature = all_features[0]\n",
    "            feature_to_be_updated = deepcopy(original_feature)\n",
    "\n",
    "            features_for_update = []\n",
    "            feature_to_be_updated.attributes['rt_object_count'] = total_count\n",
    "            feature_to_be_updated.attributes['rt_vehicle_count'] = vehicle_count\n",
    "            feature_to_be_updated.attributes['rt_car_count'] = car_count\n",
    "            feature_to_be_updated.attributes['rt_bus_count'] = bus_count\n",
    "            feature_to_be_updated.attributes['rt_truck_count'] = truck_count\n",
    "            feature_to_be_updated.attributes['rt_motorcycle_count'] = motorcycle_count\n",
    "            feature_to_be_updated.attributes['rt_pedestrian_count'] = people_count\n",
    "            feature_to_be_updated.attributes['rt_bicycle_count'] = bicycle_count\n",
    "\n",
    "            # Perform Trend Process\n",
    "            if write_trends:\n",
    "\n",
    "                # If the trend needs to be reset\n",
    "                if reset_trends and iteration==0:\n",
    "                    print(\"Resetting trends...\")\n",
    "                    feature_to_be_updated.attributes['histval_10am_th_car'] = car_count\n",
    "                    feature_to_be_updated.attributes['histval_10am_th_ped'] = people_count\n",
    "                    feature_to_be_updated.attributes['histval_10am_th_bus'] = bus_count\n",
    "                    feature_to_be_updated.attributes['histval_10am_th_trck'] = truck_count\n",
    "\n",
    "                    histval_car = car_count\n",
    "                    histval_ped = people_count\n",
    "                    histval_bus = bus_count\n",
    "                    histval_truck = truck_count\n",
    "\n",
    "                # If the trend does not need to be reset\n",
    "                else:\n",
    "                    histval_car = feature_to_be_updated.attributes['histval_10am_th_car']\n",
    "                    histval_ped = feature_to_be_updated.attributes['histval_10am_th_ped']\n",
    "                    histval_bus = feature_to_be_updated.attributes['histval_10am_th_bus']\n",
    "                    histval_truck = feature_to_be_updated.attributes['histval_10am_th_trck']\n",
    "\n",
    "                    if histval_car == None:\n",
    "                        histval_car = car_count\n",
    "                    if histval_ped == None:\n",
    "                        histval_ped = people_count                           \n",
    "                    if histval_bus == None:\n",
    "                        histval_bus = bus_count\n",
    "                    if histval_truck == None:\n",
    "                        histval_truck = truck_count\n",
    "                         \n",
    "                    # Calculate the new trend value and store it for the new update\n",
    "                    feature_to_be_updated.attributes['histval_10am_th_car'] = (car_count + histval_car) / 2\n",
    "                    feature_to_be_updated.attributes['histval_10am_th_ped'] = (people_count + histval_ped) / 2\n",
    "                    feature_to_be_updated.attributes['histval_10am_th_bus'] = (bus_count + histval_bus) / 2\n",
    "                    feature_to_be_updated.attributes['histval_10am_th_trck'] = (truck_count + histval_truck) / 2\n",
    "\n",
    "                # Determine threshold percent, value, and status\n",
    "                thrshpcnt_car = feature_to_be_updated.attributes['threshold_val_cars']\n",
    "                thrshpcnt_ped = feature_to_be_updated.attributes['threshold_val_ped']\n",
    "                thrshpcnt_bus = feature_to_be_updated.attributes['threshold_val_buses']\n",
    "                thrshpcnt_truck = feature_to_be_updated.attributes['threshold_val_trck']\n",
    "\n",
    "                # Perform writes to the threshold status field\n",
    "\n",
    "                if car_count > (histval_car * thrshpcnt_car):\n",
    "                    feature_to_be_updated.attributes['thrsh_status_cars'] = \"Above\"           \n",
    "                else:\n",
    "                    feature_to_be_updated.attributes['thrsh_status_cars'] = \"Below\"\n",
    "\n",
    "                if people_count > (histval_ped * thrshpcnt_ped):\n",
    "                    feature_to_be_updated.attributes['thrsh_status_ped'] = \"Above\"\n",
    "                else:\n",
    "                    feature_to_be_updated.attributes['thrsh_status_ped'] = \"Below\"                        \n",
    "\n",
    "                if bus_count > (histval_bus * thrshpcnt_bus):\n",
    "                    feature_to_be_updated.attributes['thrsh_status_bus'] = \"Above\"\n",
    "                else:\n",
    "                    feature_to_be_updated.attributes['thrsh_status_bus'] = \"Below\"                        \n",
    "\n",
    "                if truck_count > (histval_truck * thrshpcnt_truck):\n",
    "                    feature_to_be_updated.attributes['thrsh_status_trck'] = \"Above\"\n",
    "                    \"Passed truck thresh\"\n",
    "                else:\n",
    "                    feature_to_be_updated.attributes['thrsh_status_trck'] = \"Below\"                        \n",
    "\n",
    "            # Store attribute updates and send edit request\n",
    "            features_for_update.append(feature_to_be_updated)\n",
    "            object_point_lyr.edit_features(updates=features_for_update)    \n",
    "\n",
    "            # Perform attachment of detected image\n",
    "            scipy.misc.imsave('detected_objs.jpg', image_np)\n",
    "            obj_id = feature_to_be_updated.attributes['F__OBJECTID']\n",
    "\n",
    "            if overwrite_attachment:\n",
    "                for attachment in object_point_lyr.attachments.get_list(obj_id):\n",
    "                    object_point_lyr.attachments.delete(obj_id, attachment['id'])\n",
    "\n",
    "            object_point_lyr.attachments.add(obj_id, 'detected_objs.jpg')\n",
    "               \n",
    "            iteration+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiple Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '14th St @ Pennsylvania Ave' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 7th St' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 9th St' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '14th St @ Pennsylvania Ave' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 7th St' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 9th St' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '14th St @ Pennsylvania Ave' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 7th St' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 9th St' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '14th St @ Pennsylvania Ave' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 7th St' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 9th St' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '14th St @ Pennsylvania Ave' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 7th St' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 9th St' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '14th St @ Pennsylvania Ave' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 7th St' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 9th St' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '14th St @ Pennsylvania Ave' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 7th St' (Iteration 2)...\n",
      "Processing camera 'Pennsylvania Ave @ 9th St' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '14th St @ Pennsylvania Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '14th St @ Pennsylvania Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-62-aec51250cf6e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    153\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0moverwrite_attachment\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m                     \u001b[1;32mfor\u001b[0m \u001b[0mattachment\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mobject_point_lyr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattachments\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m                         \u001b[0mobject_point_lyr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattachments\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattachment\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m                 \u001b[0mobject_point_lyr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mattachments\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'detected_objs.jpg'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\arcgis-1.3.0-py3.6.egg\\arcgis\\features\\managers.py\u001b[0m in \u001b[0;36mdelete\u001b[1;34m(self, oid, attachment_id)\u001b[0m\n\u001b[0;32m     66\u001b[0m                \u001b[0mJSON\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m         \"\"\"\n\u001b[1;32m---> 68\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_layer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delete_attachment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattachment_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     69\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattachment_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\arcgis-1.3.0-py3.6.egg\\arcgis\\features\\layer.py\u001b[0m in \u001b[0;36m_delete_attachment\u001b[1;34m(self, oid, attachment_id)\u001b[0m\n\u001b[0;32m    163\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    164\u001b[0m             \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_url\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"/%s/deleteAttachments\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0moid\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 165\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_con\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_token\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    166\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m     \u001b[1;31m# ----------------------------------------------------------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\arcgis-1.3.0-py3.6.egg\\arcgis\\_impl\\connection.py\u001b[0m in \u001b[0;36mpost\u001b[1;34m(self, path, postdata, files, ssl, compress, is_retry, use_ordered_dict, add_token, verify_cert, token, try_json, out_folder, file_name, force_bytes, add_headers)\u001b[0m\n\u001b[0;32m   1099\u001b[0m             \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddheaders\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m             \u001b[1;31m#print(\"***\"+url)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1101\u001b[1;33m             \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoded_postdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1102\u001b[0m             resp_data, is_file = self._process_response(resp,\n\u001b[0;32m   1103\u001b[0m                                                \u001b[0mout_folder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout_folder\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    524\u001b[0m             \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 526\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    527\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    528\u001b[0m         \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    542\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    543\u001b[0m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 544\u001b[1;33m                                   '_open', req)\n\u001b[0m\u001b[0;32m    545\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    546\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    502\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    503\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 504\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    505\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1359\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1360\u001b[0m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[1;32m-> 1361\u001b[1;33m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[0;32m   1362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1363\u001b[0m         \u001b[0mhttps_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1319\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1320\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1321\u001b[1;33m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1322\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1323\u001b[0m             \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1329\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1330\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1331\u001b[1;33m                 \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1332\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1333\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mbegin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    295\u001b[0m         \u001b[1;31m# read until we get a non-100 response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    296\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 297\u001b[1;33m             \u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    298\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    299\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    257\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 258\u001b[1;33m         \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"iso-8859-1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    259\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    260\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"status line\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    584\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    585\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 586\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    587\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    588\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m   1000\u001b[0m                   \u001b[1;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1001\u001b[0m                   self.__class__)\n\u001b[1;32m-> 1002\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1003\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1004\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m    863\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Read on closed or unwrapped SSL socket.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    864\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 865\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    866\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mSSLError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    867\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mSSL_ERROR_EOF\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m    623\u001b[0m         \"\"\"\n\u001b[0;32m    624\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m             \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m             \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "event_feature_names = [\n",
    "    \"9th St @ Constitution Ave\",\n",
    "    \"12th St @ Constitution Ave\",\n",
    "    \"14th St @ Pennsylvania Ave\",\n",
    "    \"Pennsylvania Ave @ 7th St\",\n",
    "    \"Pennsylvania Ave @ 9th St\"\n",
    "]\n",
    "\n",
    "overwrite_attachment = True\n",
    "\n",
    "write_trends = True\n",
    "\n",
    "reset_trends = True\n",
    "\n",
    "with detection_graph.as_default():\n",
    "    \n",
    "    with tf.Session(graph=detection_graph) as sess:\n",
    "        \n",
    "        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
    "        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
    "        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
    "        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
    "        num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
    "\n",
    "        while True:\n",
    "            \n",
    "            for event_feature_name in event_feature_names:\n",
    "                print(\"Processing camera '{0}' (Iteration {1})...\".format(str(camera_name), str(iteration)))\n",
    "                record = ddot_df.loc[ddot_df.name == event_feature_name]\n",
    "\n",
    "                # Retrieve camera information\n",
    "                camera_name = record['name'].all()\n",
    "\n",
    "                # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
    "                image_np = np.array(ImageGrab.grab(bbox=(0,510,960,1020)))\n",
    "                image_np_expanded = np.expand_dims(image_np, axis=0)\n",
    "\n",
    "                (boxes, scores, classes, num) = sess.run(\n",
    "                      [detection_boxes, detection_scores, detection_classes, num_detections],\n",
    "                      feed_dict={image_tensor: image_np_expanded})\n",
    "\n",
    "                # Visualization of the results of a detection.\n",
    "                vis_util.visualize_boxes_and_labels_on_image_array(\n",
    "                  image_np,\n",
    "                  np.squeeze(boxes),\n",
    "                  np.squeeze(classes).astype(np.int32),\n",
    "                  np.squeeze(scores),\n",
    "                  category_index,\n",
    "                  use_normalized_coordinates=True,\n",
    "                  line_thickness=8, min_score_thresh=0.5)\n",
    "\n",
    "                cv2.imshow('object detection', cv2.resize(image_np, (960,510), interpolation=cv2.INTER_CUBIC))\n",
    "                if cv2.waitKey(25) & 0xFF == ord('q'):\n",
    "                    cv2.destroyAllWindows()\n",
    "                    break            \n",
    "\n",
    "                car_count, people_count, bicycle_count, motorcycle_count, bus_count, truck_count = object_counter(np.squeeze(classes).astype(np.int32), np.squeeze(scores))\n",
    "                vehicle_count = car_count + motorcycle_count + bus_count + truck_count\n",
    "                total_count = vehicle_count + bicycle_count + people_count\n",
    "\n",
    "                # Retrieve the feature from the feature layer to update\n",
    "                obj_fset = object_point_lyr.query(where=\"\"\"name = '\"\"\"+camera_name+\"\"\"'\"\"\", return_geometry=False)  \n",
    "                all_features = obj_fset.features\n",
    "                original_feature = all_features[0]\n",
    "                feature_to_be_updated = deepcopy(original_feature)\n",
    "\n",
    "                features_for_update = []\n",
    "                feature_to_be_updated.attributes['rt_object_count'] = total_count\n",
    "                feature_to_be_updated.attributes['rt_vehicle_count'] = vehicle_count\n",
    "                feature_to_be_updated.attributes['rt_car_count'] = car_count\n",
    "                feature_to_be_updated.attributes['rt_bus_count'] = bus_count\n",
    "                feature_to_be_updated.attributes['rt_truck_count'] = truck_count\n",
    "                feature_to_be_updated.attributes['rt_motorcycle_count'] = motorcycle_count\n",
    "                feature_to_be_updated.attributes['rt_pedestrian_count'] = people_count\n",
    "                feature_to_be_updated.attributes['rt_bicycle_count'] = bicycle_count\n",
    "\n",
    "                feature_to_be_updated.attributes['source_image'] = camera_jpeg_url\n",
    "\n",
    "                # Perform Trend Process\n",
    "                if write_trends:\n",
    "\n",
    "                    # If the trend needs to be reset\n",
    "                    if reset_trends and iteration==0:\n",
    "                        print(\"Resetting trends...\")\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_car'] = car_count\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_ped'] = people_count\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_bus'] = bus_count\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_trck'] = truck_count\n",
    "\n",
    "                        histval_car = car_count\n",
    "                        histval_ped = people_count\n",
    "                        histval_bus = bus_count\n",
    "                        histval_truck = truck_count\n",
    "\n",
    "                    # If the trend does not need to be reset\n",
    "                    else:\n",
    "                        histval_car = feature_to_be_updated.attributes['histval_10am_th_car']\n",
    "                        histval_ped = feature_to_be_updated.attributes['histval_10am_th_ped']\n",
    "                        histval_bus = feature_to_be_updated.attributes['histval_10am_th_bus']\n",
    "                        histval_truck = feature_to_be_updated.attributes['histval_10am_th_trck']\n",
    "\n",
    "                        if histval_car == None:\n",
    "                            histval_car = car_count\n",
    "                        if histval_ped == None:\n",
    "                            histval_ped = people_count                           \n",
    "                        if histval_bus == None:\n",
    "                            histval_bus = bus_count\n",
    "                        if histval_truck == None:\n",
    "                            histval_truck = truck_count\n",
    "\n",
    "                        # Calculate the new trend value and store it for the new update\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_car'] = (car_count + histval_car) / 2\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_ped'] = (people_count + histval_ped) / 2\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_bus'] = (bus_count + histval_bus) / 2\n",
    "                        feature_to_be_updated.attributes['histval_10am_th_trck'] = (truck_count + histval_truck) / 2\n",
    "\n",
    "                    # Determine threshold percent, value, and status\n",
    "                    thrshpcnt_car = feature_to_be_updated.attributes['threshold_val_cars']\n",
    "                    thrshpcnt_ped = feature_to_be_updated.attributes['threshold_val_ped']\n",
    "                    thrshpcnt_bus = feature_to_be_updated.attributes['threshold_val_buses']\n",
    "                    thrshpcnt_truck = feature_to_be_updated.attributes['threshold_val_trck']\n",
    "\n",
    "                    # Perform writes to the threshold status field\n",
    "\n",
    "                    if car_count > (histval_car * thrshpcnt_car):\n",
    "                        feature_to_be_updated.attributes['thrsh_status_cars'] = \"Above\"           \n",
    "                    else:\n",
    "                        feature_to_be_updated.attributes['thrsh_status_cars'] = \"Below\"\n",
    "\n",
    "                    if people_count > (histval_ped * thrshpcnt_ped):\n",
    "                        feature_to_be_updated.attributes['thrsh_status_ped'] = \"Above\"\n",
    "                    else:\n",
    "                        feature_to_be_updated.attributes['thrsh_status_ped'] = \"Below\"                        \n",
    "\n",
    "                    if bus_count > (histval_bus * thrshpcnt_bus):\n",
    "                        feature_to_be_updated.attributes['thrsh_status_bus'] = \"Above\"\n",
    "                    else:\n",
    "                        feature_to_be_updated.attributes['thrsh_status_bus'] = \"Below\"                        \n",
    "\n",
    "                    if truck_count > (histval_truck * thrshpcnt_truck):\n",
    "                        feature_to_be_updated.attributes['thrsh_status_trck'] = \"Above\"\n",
    "                    else:\n",
    "                        feature_to_be_updated.attributes['thrsh_status_trck'] = \"Below\"                        \n",
    "\n",
    "                # Store attribute updates and send edit request\n",
    "                features_for_update.append(feature_to_be_updated)\n",
    "                object_point_lyr.edit_features(updates=features_for_update)    \n",
    "\n",
    "                # Perform attachment of detected image\n",
    "                scipy.misc.imsave('detected_objs.jpg', image_np)\n",
    "                obj_id = feature_to_be_updated.attributes['F__OBJECTID']\n",
    "\n",
    "                if overwrite_attachment:\n",
    "                    for attachment in object_point_lyr.attachments.get_list(obj_id):\n",
    "                        object_point_lyr.attachments.delete(obj_id, attachment['id'])\n",
    "\n",
    "                object_point_lyr.attachments.add(obj_id, 'detected_objs.jpg')\n",
    "\n",
    "                if upload_source:\n",
    "                    object_point_lyr.attachments.add(obj_id, 'source.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Demo Event Features Against Traffic Cameras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing camera '12th St @ Constitution Ave' (Iteration 1)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 1)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 2)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 3)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 3)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 4)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 4)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 5)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 5)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 6)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 6)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 7)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 7)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 8)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 8)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 9)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 9)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 10)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 10)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 11)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 11)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 12)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 12)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 13)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 13)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 14)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 14)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 15)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 15)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 16)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 16)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 17)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 17)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 18)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 18)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 19)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 19)...\n",
      "Processing camera '12th St @ Constitution Ave' (Iteration 20)...\n",
      "Processing camera '9th St @ Constitution Ave' (Iteration 20)...\n"
     ]
    }
   ],
   "source": [
    "demo_event_features = True\n",
    "\n",
    "event_feature_names = [\n",
    "    \"9th St @ Constitution Ave\",\n",
    "    \"12th St @ Constitution Ave\"\n",
    "]\n",
    "\n",
    "overwrite_attachment = True\n",
    "\n",
    "upload_source = False\n",
    "\n",
    "write_trends = True\n",
    "\n",
    "reset_trends = True\n",
    "\n",
    "with detection_graph.as_default():\n",
    "    \n",
    "    with tf.Session(graph=detection_graph) as sess:\n",
    "        \n",
    "        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
    "        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
    "        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
    "        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
    "        num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
    "\n",
    "        for iteration in range(1, 21):\n",
    "            # TrafficLand API source JSON\n",
    "            source_json = r\"http://api.trafficland.com/v1.5/json/video_feeds?system=ddot&key=1594c8892d7fbd18181a8a6a44958b28\"\n",
    "            df = pd.read_json(source_json)\n",
    "            ddot_df = df.loc[df.provider == 'DDOT']\n",
    "            \n",
    "            if demo_event_features:\n",
    "                for event_feature_name in event_feature_names:\n",
    "                    print(\"Processing camera '{0}' (Iteration {1})...\".format(str(camera_name), str(iteration)))\n",
    "                    \n",
    "                    record = ddot_df.loc[ddot_df.name == event_feature_name]\n",
    "                    \n",
    "                    # Retrieve camera information\n",
    "                    camera_jpeg_url = record['content'].all()['hugeJpeg']\n",
    "                    camera_name = record['name'].all()\n",
    "                    camera_id = record['publicId'].all()\n",
    "\n",
    "                    # Retrieve the camera image\n",
    "                    camera_response = requests.get(camera_jpeg_url)\n",
    "\n",
    "    #                 # Perform attachment of source image\n",
    "                    if upload_source:\n",
    "                        streamed_response = requests.get(camera_jpeg_url, stream=True)\n",
    "                        with open('source.jpg', 'wb') as out_file:\n",
    "                            shutil.copyfileobj(streamed_response.raw, out_file)\n",
    "        #                 del response                \n",
    "\n",
    "                    image = Image.open(BytesIO(camera_response.content))\n",
    "                    image_np = load_image_into_numpy_array(image)\n",
    "                    image_np_expanded = np.expand_dims(image_np, axis=0)\n",
    "\n",
    "                    (boxes, scores, classes, num) = sess.run(\n",
    "                          [detection_boxes, detection_scores, detection_classes, num_detections],\n",
    "                          feed_dict={image_tensor: image_np_expanded})\n",
    "\n",
    "                    vis_util.visualize_boxes_and_labels_on_image_array(\n",
    "                          image_np,\n",
    "                          np.squeeze(boxes),\n",
    "                          np.squeeze(classes).astype(np.int32),\n",
    "                          np.squeeze(scores),\n",
    "                          category_index,\n",
    "                          use_normalized_coordinates=True,\n",
    "                          line_thickness=8)\n",
    "\n",
    "                    car_count, people_count, bicycle_count, motorcycle_count, bus_count, truck_count = object_counter(np.squeeze(classes).astype(np.int32), np.squeeze(scores))\n",
    "                    vehicle_count = car_count + motorcycle_count + bus_count + truck_count\n",
    "                    total_count = vehicle_count + bicycle_count + people_count\n",
    "\n",
    "                    # Retrieve the feature from the feature layer to update\n",
    "                    obj_fset = object_point_lyr.query(where=\"\"\"name = '\"\"\"+camera_name+\"\"\"'\"\"\", return_geometry=False)  \n",
    "                    all_features = obj_fset.features\n",
    "                    original_feature = all_features[0]\n",
    "                    feature_to_be_updated = deepcopy(original_feature)\n",
    "\n",
    "                    features_for_update = []\n",
    "                    feature_to_be_updated.attributes['rt_object_count'] = total_count\n",
    "                    feature_to_be_updated.attributes['rt_vehicle_count'] = vehicle_count\n",
    "                    feature_to_be_updated.attributes['rt_car_count'] = car_count\n",
    "                    feature_to_be_updated.attributes['rt_bus_count'] = bus_count\n",
    "                    feature_to_be_updated.attributes['rt_truck_count'] = truck_count\n",
    "                    feature_to_be_updated.attributes['rt_motorcycle_count'] = motorcycle_count\n",
    "                    feature_to_be_updated.attributes['rt_pedestrian_count'] = people_count\n",
    "                    feature_to_be_updated.attributes['rt_bicycle_count'] = bicycle_count\n",
    "\n",
    "                    feature_to_be_updated.attributes['source_image'] = camera_jpeg_url\n",
    "\n",
    "                    # Perform Trend Process\n",
    "                    if write_trends:\n",
    "\n",
    "                        # If the trend needs to be reset\n",
    "                        if reset_trends and iteration==0:\n",
    "                            print(\"Resetting trends...\")\n",
    "                            feature_to_be_updated.attributes['histval_10am_th_car'] = car_count\n",
    "                            feature_to_be_updated.attributes['histval_10am_th_ped'] = people_count\n",
    "                            feature_to_be_updated.attributes['histval_10am_th_bus'] = bus_count\n",
    "                            feature_to_be_updated.attributes['histval_10am_th_trck'] = truck_count\n",
    "\n",
    "                            histval_car = car_count\n",
    "                            histval_ped = people_count\n",
    "                            histval_bus = bus_count\n",
    "                            histval_truck = truck_count\n",
    "\n",
    "                        # If the trend does not need to be reset\n",
    "                        else:\n",
    "                            histval_car = feature_to_be_updated.attributes['histval_10am_th_car']\n",
    "                            histval_ped = feature_to_be_updated.attributes['histval_10am_th_ped']\n",
    "                            histval_bus = feature_to_be_updated.attributes['histval_10am_th_bus']\n",
    "                            histval_truck = feature_to_be_updated.attributes['histval_10am_th_trck']\n",
    "\n",
    "                            if histval_car == None:\n",
    "                                histval_car = car_count\n",
    "                            if histval_bus == None:\n",
    "                                histval_bus = bus_count\n",
    "                            if histval_truck == None:\n",
    "                                histval_truck = truck_count\n",
    "                            if histval_ped == None:\n",
    "                                histval_ped = people_count                            \n",
    "\n",
    "                            # Calculate the new trend value and store it for the new update\n",
    "                            feature_to_be_updated.attributes['histval_10am_th_car'] = (car_count + histval_car) / 2\n",
    "                            feature_to_be_updated.attributes['histval_10am_th_ped'] = (people_count + histval_ped) / 2\n",
    "                            feature_to_be_updated.attributes['histval_10am_th_bus'] = (bus_count + histval_bus) / 2\n",
    "                            feature_to_be_updated.attributes['histval_10am_th_trck'] = (truck_count + histval_truck) / 2\n",
    "\n",
    "                        # Determine threshold percent, value, and status\n",
    "                        thrshpcnt_car = feature_to_be_updated.attributes['threshold_val_cars']\n",
    "                        thrshpcnt_ped = feature_to_be_updated.attributes['threshold_val_ped']\n",
    "                        thrshpcnt_bus = feature_to_be_updated.attributes['threshold_val_buses']\n",
    "                        thrshpcnt_truck = feature_to_be_updated.attributes['threshold_val_trck']\n",
    "\n",
    "                        # Perform writes to the threshold status field\n",
    "\n",
    "                        if car_count > (histval_car * thrshpcnt_car):\n",
    "                            feature_to_be_updated.attributes['thrsh_status_cars'] = \"Above\"           \n",
    "                        else:\n",
    "                            feature_to_be_updated.attributes['thrsh_status_cars'] = \"Below\"\n",
    "\n",
    "                        if people_count > (histval_ped * thrshpcnt_ped):\n",
    "                            feature_to_be_updated.attributes['thrsh_status_ped'] = \"Above\"\n",
    "                        else:\n",
    "                            feature_to_be_updated.attributes['thrsh_status_ped'] = \"Below\"                        \n",
    "\n",
    "                        if bus_count > (histval_bus * thrshpcnt_bus):\n",
    "                            feature_to_be_updated.attributes['thrsh_status_bus'] = \"Above\"\n",
    "                        else:\n",
    "                            feature_to_be_updated.attributes['thrsh_status_bus'] = \"Below\"                        \n",
    "\n",
    "                        if truck_count > (histval_truck * thrshpcnt_truck):\n",
    "                            feature_to_be_updated.attributes['thrsh_status_trck'] = \"Above\"\n",
    "                        else:\n",
    "                            feature_to_be_updated.attributes['thrsh_status_trck'] = \"Below\"                        \n",
    "\n",
    "                    # Store attribute updates and send edit request\n",
    "                    features_for_update.append(feature_to_be_updated)\n",
    "                    object_point_lyr.edit_features(updates=features_for_update)    \n",
    "\n",
    "                    # Perform attachment of detected image\n",
    "                    scipy.misc.imsave('detected_objs.jpg', image_np)\n",
    "                    obj_id = feature_to_be_updated.attributes['F__OBJECTID']\n",
    "\n",
    "                    if overwrite_attachment:\n",
    "                        for attachment in object_point_lyr.attachments.get_list(obj_id):\n",
    "                            object_point_lyr.attachments.delete(obj_id, attachment['id'])\n",
    "\n",
    "                    object_point_lyr.attachments.add(obj_id, 'detected_objs.jpg')\n",
    "\n",
    "                    if upload_source:\n",
    "                        object_point_lyr.attachments.add(obj_id, 'source.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P2: Perform Updates Along Constitution Avenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform run on subset along Constitution Ave."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=e1437573230c4664a51a6d1e8d8d7176' target='_blank'>\n",
       "                        <img src='data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAACFCAIAAACR/CB7AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAB/0lEQVR4nO3YQU4CMQBA0anxmhzCk3AID1oXGI0aExP6GUbeW7GaFvrbaRhzzg1We9p7AvxPwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwjq8McYYY+9ZfPe89wT2dFmPOedxRxljbOfT5cOVQ6yd5+OGtXBJ9hrl4+Hbtm3n0zVDLJ/nsldhcSB3h/zPJTnuKNcr5rnmxCr25W1OlEObc3428fJ6V7/SgjX70vu25hsWz/x1iHJJbjDKkrvR8nk+7h3rfbvHl/cbjLLkycvnueYtU+zLuz3k+YulV6LV+/I2fwdQcC8m4Z93EsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEsIiISwSwiIhLBLCIiEsEm/28YQRKkb2mQAAAABJRU5ErkJggg==' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=e1437573230c4664a51a6d1e8d8d7176' target='_blank'><b>DC_TrafficCameras_ConstitutionAve</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://esrifederal.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by albe9057@esri.com_esrifederal\n",
       "                        <br/>Last Modified: February 26, 2018\n",
       "                        <br/>0 comments, 0 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"DC_TrafficCameras_ConstitutionAve\" type:Feature Layer Collection owner:albe9057@esri.com_esrifederal>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_point_srvc_const_ave = gis.content.search(\"DC_TrafficCameras_ConstitutionAve\", item_type=\"feature service\")[0]\n",
    "object_point_srvc_const_ave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Convert our existing service into a pandas dataframe\n",
    "object_point_lyr_const_ave = object_point_srvc_const_ave.layers[0]\n",
    "obj_fset_const_ave = object_point_lyr_const_ave.query()  #querying without any conditions returns all the features\n",
    "obj_df_const_ave = obj_fset_const_ave.df\n",
    "constitutionave_names_list = obj_df_const_ave.name.tolist()\n",
    "ddot_constitutionave_df = ddot_df[ddot_df.name.isin(constitutionave_names_list)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://esrifederal.maps.arcgis.com/home/webmap/viewer.html?webmap=e081b389ac8d4bdb817c34dbe78a5fc4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P3: Trend-setter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=041de891040140a898950ae441631fd2' target='_blank'>\n",
       "                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://esrifederal.maps.arcgis.com/home/item.html?id=041de891040140a898950ae441631fd2' target='_blank'><b>DC_TrafficCameras_HIST_ConstAve</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://esrifederal.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by albe9057@esri.com_esrifederal\n",
       "                        <br/>Last Modified: February 27, 2018\n",
       "                        <br/>0 comments, 4 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"DC_TrafficCameras_HIST_ConstAve\" type:Feature Layer Collection owner:albe9057@esri.com_esrifederal>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_point_srvc = gis.content.search(\"DC_TrafficCameras_HIST_ConstAve\", item_type=\"feature service\")[0]\n",
    "object_point_srvc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F__OBJECTID</th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>attribute_0</th>\n",
       "      <th>content</th>\n",
       "      <th>createdAt</th>\n",
       "      <th>description</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>name</th>\n",
       "      <th>observation_time</th>\n",
       "      <th>...</th>\n",
       "      <th>recording_time</th>\n",
       "      <th>rt_bicycle_count</th>\n",
       "      <th>rt_bus_count</th>\n",
       "      <th>rt_car_count</th>\n",
       "      <th>rt_motorcycle_count</th>\n",
       "      <th>rt_object_count</th>\n",
       "      <th>rt_pedestrian_count</th>\n",
       "      <th>rt_truck_count</th>\n",
       "      <th>rt_vehicle_count</th>\n",
       "      <th>SHAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>{'halfJpeg': 'http://ie.trafficland.com/v1.0/2...</td>\n",
       "      <td>1410450964690</td>\n",
       "      <td>None</td>\n",
       "      <td>38.892306</td>\n",
       "      <td>-77.028276</td>\n",
       "      <td>12th St @ Constitution Ave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8574748, 'y': 4706257}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>{'halfJpeg': 'http://ie.trafficland.com/v1.0/2...</td>\n",
       "      <td>1410450997585</td>\n",
       "      <td>None</td>\n",
       "      <td>38.892269</td>\n",
       "      <td>-77.024097</td>\n",
       "      <td>9th St @ Constitution Ave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8574283, 'y': 4706252}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>{'halfJpeg': 'http://ie.trafficland.com/v1.0/2...</td>\n",
       "      <td>1410451320851</td>\n",
       "      <td>None</td>\n",
       "      <td>38.892212</td>\n",
       "      <td>-77.033831</td>\n",
       "      <td>Constitution Ave @ 15th St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8575367, 'y': 4706244}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>{'halfJpeg': 'http://ie.trafficland.com/v1.0/2...</td>\n",
       "      <td>1410454639501</td>\n",
       "      <td>None</td>\n",
       "      <td>38.892221</td>\n",
       "      <td>-77.039654</td>\n",
       "      <td>Constitution Ave @ 17th St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8576015, 'y': 4706245}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>{'halfJpeg': 'http://ie.trafficland.com/v1.0/2...</td>\n",
       "      <td>1410454655458</td>\n",
       "      <td>None</td>\n",
       "      <td>38.891985</td>\n",
       "      <td>-77.049949</td>\n",
       "      <td>Constitution Ave @ 23rd St</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>{'x': -8577161, 'y': 4706211}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   F__OBJECTID  OBJECTID attribute_0  \\\n",
       "0            1         1        None   \n",
       "1            2         2        None   \n",
       "2            3         3        None   \n",
       "3            4         4        None   \n",
       "4            5         5        None   \n",
       "\n",
       "                                             content      createdAt  \\\n",
       "0  {'halfJpeg': 'http://ie.trafficland.com/v1.0/2...  1410450964690   \n",
       "1  {'halfJpeg': 'http://ie.trafficland.com/v1.0/2...  1410450997585   \n",
       "2  {'halfJpeg': 'http://ie.trafficland.com/v1.0/2...  1410451320851   \n",
       "3  {'halfJpeg': 'http://ie.trafficland.com/v1.0/2...  1410454639501   \n",
       "4  {'halfJpeg': 'http://ie.trafficland.com/v1.0/2...  1410454655458   \n",
       "\n",
       "  description   latitude  longitude                        name  \\\n",
       "0        None  38.892306 -77.028276  12th St @ Constitution Ave   \n",
       "1        None  38.892269 -77.024097   9th St @ Constitution Ave   \n",
       "2        None  38.892212 -77.033831  Constitution Ave @ 15th St   \n",
       "3        None  38.892221 -77.039654  Constitution Ave @ 17th St   \n",
       "4        None  38.891985 -77.049949  Constitution Ave @ 23rd St   \n",
       "\n",
       "   observation_time              ...               recording_time  \\\n",
       "0               NaN              ...                         None   \n",
       "1               NaN              ...                         None   \n",
       "2               NaN              ...                         None   \n",
       "3               NaN              ...                         None   \n",
       "4               NaN              ...                         None   \n",
       "\n",
       "  rt_bicycle_count rt_bus_count rt_car_count  rt_motorcycle_count  \\\n",
       "0                0            0            0                    0   \n",
       "1                0            0            0                    0   \n",
       "2                0            0            0                    0   \n",
       "3                0            0            0                    0   \n",
       "4                0            0            0                    0   \n",
       "\n",
       "  rt_object_count rt_pedestrian_count  rt_truck_count  rt_vehicle_count  \\\n",
       "0               0                   0               0                 0   \n",
       "1               0                   0               0                 0   \n",
       "2               0                   0               0                 0   \n",
       "3               0                   0               0                 0   \n",
       "4               0                   0               0                 0   \n",
       "\n",
       "                           SHAPE  \n",
       "0  {'x': -8574748, 'y': 4706257}  \n",
       "1  {'x': -8574283, 'y': 4706252}  \n",
       "2  {'x': -8575367, 'y': 4706244}  \n",
       "3  {'x': -8576015, 'y': 4706245}  \n",
       "4  {'x': -8577161, 'y': 4706211}  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert our existing service into a pandas dataframe\n",
    "object_point_lyr = object_point_srvc.layers[0]\n",
    "obj_fset = object_point_lyr.query()  #querying without any conditions returns all the features\n",
    "obj_df = obj_fset.df\n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With auto-refresh of source JSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Refresh iteration 0...\n",
      "Iteration: 0 of 100\n",
      "Iteration: 1 of 100\n",
      "Iteration: 2 of 100\n",
      "Iteration: 3 of 100\n",
      "Iteration: 4 of 100\n",
      "Iteration: 5 of 100\n",
      "Iteration: 6 of 100\n",
      "Iteration: 7 of 100\n",
      "Iteration: 8 of 100\n",
      "Iteration: 9 of 100\n",
      "Iteration: 10 of 100\n",
      "Iteration: 11 of 100\n",
      "Iteration: 12 of 100\n",
      "Iteration: 13 of 100\n",
      "Iteration: 14 of 100\n",
      "Iteration: 15 of 100\n",
      "Iteration: 16 of 100\n",
      "Iteration: 17 of 100\n",
      "Iteration: 18 of 100\n",
      "Iteration: 19 of 100\n",
      "Iteration: 20 of 100\n",
      "Iteration: 21 of 100\n",
      "Iteration: 22 of 100\n",
      "Iteration: 23 of 100\n",
      "Iteration: 24 of 100\n",
      "Iteration: 25 of 100\n",
      "Iteration: 26 of 100\n",
      "Iteration: 27 of 100\n",
      "Iteration: 28 of 100\n",
      "Iteration: 29 of 100\n",
      "Iteration: 30 of 100\n",
      "Iteration: 31 of 100\n",
      "Iteration: 32 of 100\n",
      "Iteration: 33 of 100\n",
      "Iteration: 34 of 100\n",
      "Iteration: 35 of 100\n",
      "Iteration: 36 of 100\n",
      "Iteration: 37 of 100\n",
      "Iteration: 38 of 100\n",
      "Iteration: 39 of 100\n",
      "Iteration: 40 of 100\n",
      "Iteration: 41 of 100\n",
      "Iteration: 42 of 100\n",
      "Iteration: 43 of 100\n",
      "Iteration: 44 of 100\n",
      "Iteration: 45 of 100\n",
      "Iteration: 46 of 100\n",
      "Iteration: 47 of 100\n",
      "Iteration: 48 of 100\n",
      "Iteration: 49 of 100\n",
      "Iteration: 50 of 100\n",
      "Iteration: 51 of 100\n",
      "Iteration: 52 of 100\n",
      "Iteration: 53 of 100\n",
      "Iteration: 54 of 100\n",
      "Iteration: 55 of 100\n",
      "Iteration: 56 of 100\n",
      "Iteration: 57 of 100\n",
      "Iteration: 58 of 100\n",
      "Iteration: 59 of 100\n",
      "Iteration: 60 of 100\n",
      "Iteration: 61 of 100\n",
      "Iteration: 62 of 100\n",
      "Iteration: 63 of 100\n",
      "Iteration: 64 of 100\n",
      "Iteration: 65 of 100\n",
      "Iteration: 66 of 100\n",
      "Iteration: 67 of 100\n",
      "Iteration: 68 of 100\n",
      "Iteration: 69 of 100\n",
      "Iteration: 70 of 100\n",
      "Iteration: 71 of 100\n",
      "Iteration: 72 of 100\n",
      "Iteration: 73 of 100\n",
      "Iteration: 74 of 100\n",
      "Iteration: 75 of 100\n",
      "Iteration: 76 of 100\n",
      "Iteration: 77 of 100\n",
      "Iteration: 78 of 100\n",
      "Iteration: 79 of 100\n",
      "Iteration: 80 of 100\n",
      "Iteration: 81 of 100\n",
      "Iteration: 82 of 100\n",
      "Iteration: 83 of 100\n",
      "Iteration: 84 of 100\n",
      "Iteration: 85 of 100\n",
      "Iteration: 86 of 100\n",
      "Iteration: 87 of 100\n",
      "Iteration: 88 of 100\n",
      "Iteration: 89 of 100\n",
      "Iteration: 90 of 100\n",
      "Iteration: 91 of 100\n",
      "Iteration: 92 of 100\n",
      "Iteration: 93 of 100\n",
      "Iteration: 94 of 100\n",
      "Iteration: 95 of 100\n",
      "Iteration: 96 of 100\n",
      "Iteration: 97 of 100\n",
      "Iteration: 98 of 100\n",
      "Iteration: 99 of 100\n",
      "Refresh iteration 1...\n",
      "Iteration: 0 of 100\n",
      "Iteration: 1 of 100\n",
      "Iteration: 2 of 100\n",
      "Iteration: 3 of 100\n",
      "Iteration: 4 of 100\n",
      "Iteration: 5 of 100\n",
      "Iteration: 6 of 100\n",
      "Iteration: 7 of 100\n",
      "Iteration: 8 of 100\n",
      "Iteration: 9 of 100\n",
      "Iteration: 10 of 100\n",
      "Iteration: 11 of 100\n",
      "Iteration: 12 of 100\n",
      "Iteration: 13 of 100\n",
      "Iteration: 14 of 100\n",
      "Iteration: 15 of 100\n",
      "Iteration: 16 of 100\n",
      "Iteration: 17 of 100\n",
      "Iteration: 18 of 100\n",
      "Iteration: 19 of 100\n",
      "Iteration: 20 of 100\n",
      "Iteration: 21 of 100\n",
      "Iteration: 22 of 100\n",
      "Iteration: 23 of 100\n",
      "Iteration: 24 of 100\n",
      "Iteration: 25 of 100\n",
      "Iteration: 26 of 100\n",
      "Iteration: 27 of 100\n",
      "Iteration: 28 of 100\n",
      "Iteration: 29 of 100\n",
      "Iteration: 30 of 100\n",
      "Iteration: 31 of 100\n",
      "Iteration: 32 of 100\n",
      "Iteration: 33 of 100\n",
      "Iteration: 34 of 100\n",
      "Iteration: 35 of 100\n",
      "Iteration: 36 of 100\n",
      "Iteration: 37 of 100\n",
      "Iteration: 38 of 100\n",
      "Iteration: 39 of 100\n",
      "Iteration: 40 of 100\n",
      "Iteration: 41 of 100\n",
      "Iteration: 42 of 100\n",
      "Iteration: 43 of 100\n",
      "Iteration: 44 of 100\n",
      "Iteration: 45 of 100\n",
      "Iteration: 46 of 100\n",
      "Iteration: 47 of 100\n",
      "Iteration: 48 of 100\n",
      "Iteration: 49 of 100\n",
      "Iteration: 50 of 100\n",
      "Iteration: 51 of 100\n",
      "Iteration: 52 of 100\n",
      "Iteration: 53 of 100\n",
      "Iteration: 54 of 100\n",
      "Iteration: 55 of 100\n",
      "Iteration: 56 of 100\n",
      "Iteration: 57 of 100\n",
      "Iteration: 58 of 100\n",
      "Iteration: 59 of 100\n",
      "Iteration: 60 of 100\n",
      "Iteration: 61 of 100\n",
      "Iteration: 62 of 100\n",
      "Iteration: 63 of 100\n",
      "Iteration: 64 of 100\n",
      "Iteration: 65 of 100\n",
      "Iteration: 66 of 100\n",
      "Iteration: 67 of 100\n",
      "Iteration: 68 of 100\n",
      "Iteration: 69 of 100\n",
      "Iteration: 70 of 100\n",
      "Iteration: 71 of 100\n",
      "Iteration: 72 of 100\n",
      "Iteration: 73 of 100\n",
      "Iteration: 74 of 100\n",
      "Iteration: 75 of 100\n",
      "Iteration: 76 of 100\n",
      "Iteration: 77 of 100\n",
      "Iteration: 78 of 100\n",
      "Iteration: 79 of 100\n",
      "Iteration: 80 of 100\n",
      "Iteration: 81 of 100\n",
      "Iteration: 82 of 100\n",
      "Iteration: 83 of 100\n",
      "Iteration: 84 of 100\n",
      "Iteration: 85 of 100\n",
      "Iteration: 86 of 100\n",
      "Iteration: 87 of 100\n",
      "Iteration: 88 of 100\n",
      "Iteration: 89 of 100\n",
      "Iteration: 90 of 100\n",
      "Iteration: 91 of 100\n",
      "Iteration: 92 of 100\n",
      "Iteration: 93 of 100\n",
      "Iteration: 94 of 100\n",
      "Iteration: 95 of 100\n",
      "Iteration: 96 of 100\n",
      "Iteration: 97 of 100\n",
      "Iteration: 98 of 100\n",
      "Iteration: 99 of 100\n",
      "Refresh iteration 2...\n",
      "Iteration: 0 of 100\n",
      "Iteration: 1 of 100\n",
      "Iteration: 2 of 100\n",
      "Iteration: 3 of 100\n",
      "Iteration: 4 of 100\n",
      "Iteration: 5 of 100\n",
      "Iteration: 6 of 100\n",
      "Iteration: 7 of 100\n",
      "Iteration: 8 of 100\n",
      "Iteration: 9 of 100\n",
      "Iteration: 10 of 100\n",
      "Iteration: 11 of 100\n",
      "Iteration: 12 of 100\n",
      "Iteration: 13 of 100\n",
      "Iteration: 14 of 100\n",
      "Iteration: 15 of 100\n",
      "Iteration: 16 of 100\n",
      "Iteration: 17 of 100\n",
      "Iteration: 18 of 100\n",
      "Iteration: 19 of 100\n",
      "Iteration: 20 of 100\n",
      "Iteration: 21 of 100\n",
      "Iteration: 22 of 100\n",
      "Iteration: 23 of 100\n",
      "Iteration: 24 of 100\n",
      "Iteration: 25 of 100\n",
      "Iteration: 26 of 100\n",
      "Iteration: 27 of 100\n",
      "Iteration: 28 of 100\n",
      "Iteration: 29 of 100\n",
      "Iteration: 30 of 100\n",
      "Iteration: 31 of 100\n",
      "Iteration: 32 of 100\n",
      "Iteration: 33 of 100\n",
      "Iteration: 34 of 100\n",
      "Iteration: 35 of 100\n",
      "Iteration: 36 of 100\n",
      "Iteration: 37 of 100\n",
      "Iteration: 38 of 100\n",
      "Iteration: 39 of 100\n",
      "Iteration: 40 of 100\n",
      "Iteration: 41 of 100\n",
      "Iteration: 42 of 100\n",
      "Iteration: 43 of 100\n",
      "Iteration: 44 of 100\n",
      "Iteration: 45 of 100\n",
      "Iteration: 46 of 100\n",
      "Iteration: 47 of 100\n",
      "Iteration: 48 of 100\n",
      "Iteration: 49 of 100\n",
      "Iteration: 50 of 100\n",
      "Iteration: 51 of 100\n",
      "Iteration: 52 of 100\n",
      "Iteration: 53 of 100\n",
      "Iteration: 54 of 100\n",
      "Iteration: 55 of 100\n",
      "Iteration: 56 of 100\n",
      "Iteration: 57 of 100\n",
      "Iteration: 58 of 100\n",
      "Iteration: 59 of 100\n",
      "Iteration: 60 of 100\n",
      "Iteration: 61 of 100\n",
      "Iteration: 62 of 100\n",
      "Iteration: 63 of 100\n",
      "Iteration: 64 of 100\n",
      "Iteration: 65 of 100\n",
      "Iteration: 66 of 100\n",
      "Iteration: 67 of 100\n",
      "Iteration: 68 of 100\n",
      "Iteration: 69 of 100\n",
      "Iteration: 70 of 100\n",
      "Iteration: 71 of 100\n",
      "Iteration: 72 of 100\n",
      "Iteration: 73 of 100\n",
      "Iteration: 74 of 100\n",
      "Iteration: 75 of 100\n",
      "Iteration: 76 of 100\n",
      "Iteration: 77 of 100\n",
      "Iteration: 78 of 100\n",
      "Iteration: 79 of 100\n",
      "Iteration: 80 of 100\n",
      "Iteration: 81 of 100\n",
      "Iteration: 82 of 100\n",
      "Iteration: 83 of 100\n",
      "Iteration: 84 of 100\n",
      "Iteration: 85 of 100\n",
      "Iteration: 86 of 100\n",
      "Iteration: 87 of 100\n",
      "Iteration: 88 of 100\n",
      "Iteration: 89 of 100\n",
      "Iteration: 90 of 100\n",
      "Iteration: 91 of 100\n",
      "Iteration: 92 of 100\n",
      "Iteration: 93 of 100\n",
      "Iteration: 94 of 100\n",
      "Iteration: 95 of 100\n",
      "Iteration: 96 of 100\n",
      "Iteration: 97 of 100\n",
      "Iteration: 98 of 100\n",
      "Iteration: 99 of 100\n",
      "Refresh iteration 3...\n",
      "Iteration: 0 of 100\n",
      "Iteration: 1 of 100\n",
      "Iteration: 2 of 100\n",
      "Iteration: 3 of 100\n",
      "Iteration: 4 of 100\n",
      "Iteration: 5 of 100\n",
      "Iteration: 6 of 100\n",
      "Iteration: 7 of 100\n",
      "Iteration: 8 of 100\n",
      "Iteration: 9 of 100\n",
      "Iteration: 10 of 100\n",
      "Iteration: 11 of 100\n",
      "Iteration: 12 of 100\n",
      "Iteration: 13 of 100\n",
      "Iteration: 14 of 100\n",
      "Iteration: 15 of 100\n",
      "Iteration: 16 of 100\n",
      "Iteration: 17 of 100\n",
      "Iteration: 18 of 100\n",
      "Iteration: 19 of 100\n",
      "Iteration: 20 of 100\n",
      "Iteration: 21 of 100\n",
      "Iteration: 22 of 100\n",
      "Iteration: 23 of 100\n",
      "Iteration: 24 of 100\n",
      "Iteration: 25 of 100\n",
      "Iteration: 26 of 100\n",
      "Iteration: 27 of 100\n",
      "Iteration: 28 of 100\n",
      "Iteration: 29 of 100\n",
      "Iteration: 30 of 100\n",
      "Iteration: 31 of 100\n",
      "Iteration: 32 of 100\n",
      "Iteration: 33 of 100\n",
      "Iteration: 34 of 100\n",
      "Iteration: 35 of 100\n",
      "Iteration: 36 of 100\n",
      "Iteration: 37 of 100\n",
      "Iteration: 38 of 100\n",
      "Iteration: 39 of 100\n",
      "Iteration: 40 of 100\n",
      "Iteration: 41 of 100\n",
      "Iteration: 42 of 100\n",
      "Iteration: 43 of 100\n",
      "Iteration: 44 of 100\n",
      "Iteration: 45 of 100\n",
      "Iteration: 46 of 100\n",
      "Iteration: 47 of 100\n",
      "Iteration: 48 of 100\n",
      "Iteration: 49 of 100\n",
      "Iteration: 50 of 100\n",
      "Iteration: 51 of 100\n",
      "Iteration: 52 of 100\n",
      "Iteration: 53 of 100\n",
      "Iteration: 54 of 100\n",
      "Iteration: 55 of 100\n",
      "Iteration: 56 of 100\n",
      "Iteration: 57 of 100\n",
      "Iteration: 58 of 100\n",
      "Iteration: 59 of 100\n",
      "Iteration: 60 of 100\n",
      "Iteration: 61 of 100\n",
      "Iteration: 62 of 100\n",
      "Iteration: 63 of 100\n",
      "Iteration: 64 of 100\n",
      "Iteration: 65 of 100\n",
      "Iteration: 66 of 100\n",
      "Iteration: 67 of 100\n",
      "Iteration: 68 of 100\n",
      "Iteration: 69 of 100\n",
      "Iteration: 70 of 100\n",
      "Iteration: 71 of 100\n",
      "Iteration: 72 of 100\n",
      "Iteration: 73 of 100\n",
      "Iteration: 74 of 100\n",
      "Iteration: 75 of 100\n",
      "Iteration: 76 of 100\n",
      "Iteration: 77 of 100\n",
      "Iteration: 78 of 100\n",
      "Iteration: 79 of 100\n",
      "Iteration: 80 of 100\n",
      "Iteration: 81 of 100\n",
      "Iteration: 82 of 100\n",
      "Iteration: 83 of 100\n",
      "Iteration: 84 of 100\n",
      "Iteration: 85 of 100\n",
      "Iteration: 86 of 100\n",
      "Iteration: 87 of 100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration: 88 of 100\n",
      "Iteration: 89 of 100\n",
      "Iteration: 90 of 100\n",
      "Iteration: 91 of 100\n",
      "Iteration: 92 of 100\n",
      "Iteration: 93 of 100\n",
      "Iteration: 94 of 100\n",
      "Iteration: 95 of 100\n",
      "Iteration: 96 of 100\n",
      "Iteration: 97 of 100\n",
      "Iteration: 98 of 100\n",
      "Iteration: 99 of 100\n",
      "Refresh iteration 4...\n",
      "Iteration: 0 of 100\n",
      "Iteration: 1 of 100\n",
      "Iteration: 2 of 100\n",
      "Iteration: 3 of 100\n",
      "Iteration: 4 of 100\n",
      "Iteration: 5 of 100\n",
      "Iteration: 6 of 100\n",
      "Iteration: 7 of 100\n",
      "Iteration: 8 of 100\n",
      "Iteration: 9 of 100\n",
      "Iteration: 10 of 100\n",
      "Iteration: 11 of 100\n",
      "Iteration: 12 of 100\n",
      "Iteration: 13 of 100\n",
      "Iteration: 14 of 100\n",
      "Iteration: 15 of 100\n",
      "Iteration: 16 of 100\n",
      "Iteration: 17 of 100\n",
      "Iteration: 18 of 100\n",
      "Iteration: 19 of 100\n",
      "Iteration: 20 of 100\n",
      "Iteration: 21 of 100\n",
      "Iteration: 22 of 100\n",
      "Iteration: 23 of 100\n",
      "Iteration: 24 of 100\n",
      "Iteration: 25 of 100\n",
      "Iteration: 26 of 100\n",
      "Iteration: 27 of 100\n",
      "Iteration: 28 of 100\n",
      "Iteration: 29 of 100\n",
      "Iteration: 30 of 100\n",
      "Iteration: 31 of 100\n",
      "Iteration: 32 of 100\n",
      "Iteration: 33 of 100\n",
      "Iteration: 34 of 100\n",
      "Iteration: 35 of 100\n",
      "Iteration: 36 of 100\n",
      "Iteration: 37 of 100\n",
      "Iteration: 38 of 100\n",
      "Iteration: 39 of 100\n",
      "Iteration: 40 of 100\n",
      "Iteration: 41 of 100\n",
      "Iteration: 42 of 100\n",
      "Iteration: 43 of 100\n",
      "Iteration: 44 of 100\n",
      "Iteration: 45 of 100\n",
      "Iteration: 46 of 100\n",
      "Iteration: 47 of 100\n",
      "Iteration: 48 of 100\n",
      "Iteration: 49 of 100\n",
      "Iteration: 50 of 100\n",
      "Iteration: 51 of 100\n",
      "Iteration: 52 of 100\n",
      "Iteration: 53 of 100\n",
      "Iteration: 54 of 100\n",
      "Iteration: 55 of 100\n",
      "Iteration: 56 of 100\n",
      "Iteration: 57 of 100\n",
      "Iteration: 58 of 100\n",
      "Iteration: 59 of 100\n",
      "Iteration: 60 of 100\n",
      "Iteration: 61 of 100\n",
      "Iteration: 62 of 100\n",
      "Iteration: 63 of 100\n",
      "Iteration: 64 of 100\n",
      "Iteration: 65 of 100\n",
      "Iteration: 66 of 100\n",
      "Iteration: 67 of 100\n",
      "Iteration: 68 of 100\n",
      "Iteration: 69 of 100\n",
      "Iteration: 70 of 100\n",
      "Iteration: 71 of 100\n",
      "Iteration: 72 of 100\n",
      "Iteration: 73 of 100\n",
      "Iteration: 74 of 100\n",
      "Iteration: 75 of 100\n",
      "Iteration: 76 of 100\n",
      "Iteration: 77 of 100\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-9afdf5bb37e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     54\u001b[0m                     \u001b[1;31m# Retrieve the feature from the feature layer to update\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m                     \u001b[0mobj_fset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobject_point_lyr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquery\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwhere\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\"\"name = '\"\"\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mcamera_name\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m\"\"\"'\"\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     56\u001b[0m                     \u001b[0mall_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj_fset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m                     \u001b[0moriginal_feature\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_features\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\arcgis-1.3.0-py3.6.egg\\arcgis\\features\\layer.py\u001b[0m in \u001b[0;36mquery\u001b[1;34m(self, where, out_fields, time_filter, geometry_filter, return_geometry, return_count_only, return_ids_only, return_distinct_values, return_extent_only, group_by_fields_for_statistics, statistic_filter, result_offset, result_record_count, object_ids, distance, units, max_allowable_offset, out_sr, geometry_precision, gdb_version, order_by_fields, out_statistics, return_z, return_m, multipatch_option, quanitization_parameters, return_centroid, return_all_records, **kwargs)\u001b[0m\n\u001b[0;32m    485\u001b[0m             \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    486\u001b[0m                 \u001b[0mparams\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'resultOffset'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmax_records\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 487\u001b[1;33m                 \u001b[0mrecords\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_query\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    488\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    489\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\arcgis-1.3.0-py3.6.egg\\arcgis\\features\\layer.py\u001b[0m in \u001b[0;36m_query\u001b[1;34m(self, url, params)\u001b[0m\n\u001b[0;32m   1004\u001b[0m         \u001b[1;34m\"\"\" returns results of query \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1005\u001b[0m         result = self._con.post(path=url,\n\u001b[1;32m-> 1006\u001b[1;33m                                 postdata=params, token=self._token)\n\u001b[0m\u001b[0;32m   1007\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m'error'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1008\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\arcgis-1.3.0-py3.6.egg\\arcgis\\_impl\\connection.py\u001b[0m in \u001b[0;36mpost\u001b[1;34m(self, path, postdata, files, ssl, compress, is_retry, use_ordered_dict, add_token, verify_cert, token, try_json, out_folder, file_name, force_bytes, add_headers)\u001b[0m\n\u001b[0;32m   1099\u001b[0m             \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddheaders\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1100\u001b[0m             \u001b[1;31m#print(\"***\"+url)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1101\u001b[1;33m             \u001b[0mresp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoded_postdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1102\u001b[0m             resp_data, is_file = self._process_response(resp,\n\u001b[0;32m   1103\u001b[0m                                                \u001b[0mout_folder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mout_folder\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    524\u001b[0m             \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    525\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 526\u001b[1;33m         \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    527\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    528\u001b[0m         \u001b[1;31m# post-process response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, req, data)\u001b[0m\n\u001b[0;32m    542\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    543\u001b[0m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[1;32m--> 544\u001b[1;33m                                   '_open', req)\n\u001b[0m\u001b[0;32m    545\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    546\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    502\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    503\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 504\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    505\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[1;34m(self, req)\u001b[0m\n\u001b[0;32m   1359\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1360\u001b[0m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[1;32m-> 1361\u001b[1;33m                 context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[0;32m   1362\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1363\u001b[0m         \u001b[0mhttps_request\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mdo_open\u001b[1;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[0;32m   1319\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# timeout error\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1320\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1321\u001b[1;33m             \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1322\u001b[0m         \u001b[1;32mexcept\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1323\u001b[0m             \u001b[0mh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1329\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1330\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1331\u001b[1;33m                 \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1332\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1333\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mbegin\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    295\u001b[0m         \u001b[1;31m# read until we get a non-100 response\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    296\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 297\u001b[1;33m             \u001b[0mversion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    298\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    299\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_read_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    257\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 258\u001b[1;33m         \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"iso-8859-1\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    259\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    260\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"status line\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    584\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    585\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 586\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    587\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    588\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m   1000\u001b[0m                   \u001b[1;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1001\u001b[0m                   self.__class__)\n\u001b[1;32m-> 1002\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1003\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1004\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m    863\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Read on closed or unwrapped SSL socket.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    864\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 865\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    866\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mSSLError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    867\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mSSL_ERROR_EOF\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m    623\u001b[0m         \"\"\"\n\u001b[0;32m    624\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m             \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m             \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "with detection_graph.as_default():\n",
    "    \n",
    "    with tf.Session(graph=detection_graph) as sess:\n",
    "        \n",
    "        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n",
    "        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n",
    "        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')\n",
    "        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')\n",
    "        num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n",
    "        \n",
    "        for refresh_iteration in range(10):\n",
    "            print(\"Refresh iteration {0}...\".format(str(refresh_iteration)))\n",
    "            \n",
    "            # TrafficLand API source JSON\n",
    "            source_json = r\"http://api.trafficland.com/v1.5/json/video_feeds?system=ddot&key=1594c8892d7fbd18181a8a6a44958b28\"\n",
    "            df = pd.read_json(source_json)\n",
    "            ddot_df = df.loc[df.provider == 'DDOT']\n",
    "            ddot_constitutionave_df = ddot_df[ddot_df.name.isin(constitutionave_names_list)]\n",
    "            \n",
    "            for iteration in range(100):\n",
    "                print(\"Iteration: {0} of {1}\".format(str(iteration), str(100)))\n",
    "\n",
    "                for ix, row in enumerate(ddot_constitutionave_df.iterrows()):\n",
    "\n",
    "                    # Retrieve camera information\n",
    "                    camera_jpeg_url = ddot_constitutionave_df.iloc[ix]['content']['hugeJpeg']\n",
    "                    camera_name = ddot_constitutionave_df.iloc[ix]['name']\n",
    "                    camera_id = ddot_constitutionave_df.iloc[ix]['publicId']\n",
    "\n",
    "                    # Retrieve the camera image\n",
    "                    camera_response = requests.get(camera_jpeg_url)\n",
    "\n",
    "                    image = Image.open(BytesIO(camera_response.content))\n",
    "                    image_np = load_image_into_numpy_array(image)\n",
    "                    image_np_expanded = np.expand_dims(image_np, axis=0)\n",
    "\n",
    "                    (boxes, scores, classes, num) = sess.run(\n",
    "                          [detection_boxes, detection_scores, detection_classes, num_detections],\n",
    "                          feed_dict={image_tensor: image_np_expanded})\n",
    "\n",
    "                    vis_util.visualize_boxes_and_labels_on_image_array(\n",
    "                          image_np,\n",
    "                          np.squeeze(boxes),\n",
    "                          np.squeeze(classes).astype(np.int32),\n",
    "                          np.squeeze(scores),\n",
    "                          category_index,\n",
    "                          use_normalized_coordinates=True,\n",
    "                          line_thickness=8)\n",
    "\n",
    "                    car_count, people_count, bicycle_count, motorcycle_count, bus_count, truck_count = object_counter(np.squeeze(classes).astype(np.int32), np.squeeze(scores))\n",
    "                    vehicle_count = car_count + motorcycle_count + bus_count + truck_count\n",
    "                    total_count = vehicle_count + bicycle_count + people_count\n",
    "\n",
    "                    # Retrieve the feature from the feature layer to update\n",
    "                    obj_fset = object_point_lyr.query(where=\"\"\"name = '\"\"\"+camera_name+\"\"\"'\"\"\")  \n",
    "                    all_features = obj_fset.features\n",
    "                    original_feature = all_features[0]\n",
    "\n",
    "                    features_to_be_added = []\n",
    "                    new_feature = deepcopy(original_feature)\n",
    "\n",
    "                    new_feature.attributes['rt_object_count'] = total_count\n",
    "                    new_feature.attributes['rt_vehicle_count'] = vehicle_count\n",
    "                    new_feature.attributes['rt_car_count'] = car_count\n",
    "                    new_feature.attributes['rt_bus_count'] = bus_count\n",
    "                    new_feature.attributes['rt_truck_count'] = truck_count\n",
    "                    new_feature.attributes['rt_motorcycle_count'] = motorcycle_count\n",
    "                    new_feature.attributes['rt_pedestrian_count'] = people_count\n",
    "                    new_feature.attributes['rt_bicycle_count'] = bicycle_count\n",
    "                    new_feature.attributes['recording_id'] = \"Cam{0}_{1}\".format(str(ix),\n",
    "                                                                                 str(datetime.now().strftime(\"%m%d%y_%H%M%S\")))\n",
    "                    new_feature.attributes['observation_time'] = datetime.now().strftime(\"%m/%d/%y %H:%M:%S\")\n",
    "\n",
    "                    #add this to the list of features to be updated\n",
    "                    features_to_be_added.append(new_feature)\n",
    "\n",
    "                    object_point_lyr.edit_features(adds = features_to_be_added) "
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}