TensorFlow_ObjectDetection_Demo_Atlanta_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": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "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": 4,
   "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": 5,
   "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": 6,
   "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": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# What model to download.\n",
    "MODEL_NAME = 'ssd_inception_v2_coco_2017_11_17'\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": 8,
   "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": 9,
   "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": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'id': 1, 'name': 'person'},\n",
       " {'id': 2, 'name': 'bicycle'},\n",
       " {'id': 3, 'name': 'car'},\n",
       " {'id': 4, 'name': 'motorcycle'},\n",
       " {'id': 6, 'name': 'bus'},\n",
       " {'id': 7, 'name': 'train'},\n",
       " {'id': 8, 'name': 'truck'}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def object_counter(classes_arr, scores_arr, score_thresh=0.2):\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": [
    "# Multiple Camera Iteration Prototyping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P1: Use existing feature service and send model outputs as updates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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=316526a4b4da4b10b101bdf6ae4bed8f' target='_blank'>\n",
       "                        <img 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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=316526a4b4da4b10b101bdf6ae4bed8f' target='_blank'><b>CobbCountyCameras_v01</b>\n",
       "                        </a>\n",
       "                        <br/>Test<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: May 16, 2018\n",
       "                        <br/>0 comments, 4 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"CobbCountyCameras_v01\" type:Feature Layer Collection owner:albe9057@esri.com_esrifederal>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_point_srvc = gis.content.search(\"CobbCountyCameras_v01\", 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": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>caac3512-ecac-46ad-9c1a-c213f0711a36</td>\n",
       "      <td>117</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>https://www.cobbcommute.org/CameraProxy/Index?...</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>None</td>\n",
       "      <td>{'x': -9403134.5081, 'y': 4013664.1466000006}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>a917d63d-d3f3-4705-a65e-5f90adeeb0aa</td>\n",
       "      <td>118</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>https://www.cobbcommute.org/CameraProxy/Index?...</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>None</td>\n",
       "      <td>{'x': -9403081.127, 'y': 4013706.7436000034}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>92d90037-6e61-415d-9e6e-600fe08b5ad8</td>\n",
       "      <td>120</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>https://www.cobbcommute.org/CameraProxy/Index?...</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>None</td>\n",
       "      <td>{'x': -9403213.4338, 'y': 4013273.2920999974}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>471b9152-9cda-4b42-a280-06a0df6a2109</td>\n",
       "      <td>121</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>https://www.cobbcommute.org/CameraProxy/Index?...</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>None</td>\n",
       "      <td>{'x': -9402963.2101, 'y': 4013196.6242000014}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>f91bd4b3-9fc6-4f31-a1a9-c562124782c1</td>\n",
       "      <td>122</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>https://www.cobbcommute.org/CameraProxy/Index?...</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>None</td>\n",
       "      <td>{'x': -9403450.5928, 'y': 4013687.2523000017}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>5e9393aa-b144-41f0-b2bf-7cfcc303c49b</td>\n",
       "      <td>123</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>https://www.cobbcommute.org/CameraProxy/Index?...</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>None</td>\n",
       "      <td>{'x': -9403674.4942, 'y': 4013871.311999999}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>7cec17bb-0ddb-4f77-b311-8277bdfcd95e</td>\n",
       "      <td>124</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>https://www.cobbcommute.org/CameraProxy/Index?...</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>None</td>\n",
       "      <td>{'x': -9403815.719, 'y': 4014130.0661000013}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>91c52ea1-dc09-4f0e-bad5-650a5a710c9d</td>\n",
       "      <td>125</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>https://www.cobbcommute.org/CameraProxy/Index?...</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>None</td>\n",
       "      <td>{'x': -9403157.46, 'y': 4014853.7245000005}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>e05dbc40-07ba-43aa-8b7e-d58eb4abc7c9</td>\n",
       "      <td>126</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>https://www.cobbcommute.org/CameraProxy/Index?...</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>Below</td>\n",
       "      <td>None</td>\n",
       "      <td>{'x': -9405079.1592, 'y': 4015780.6895999983}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CreationDate Creator EditDate Editor                              GlobalID  \\\n",
       "0          None    None     None   None  6e3947ed-7c39-46f2-8c54-80c8e885ef12   \n",
       "1          None    None     None   None  e5aa01ee-bf36-4a94-a652-8155e478481a   \n",
       "2          None    None     None   None  44dfaa2f-31db-440a-9639-6183e6d63c7f   \n",
       "3          None    None     None   None  d8f03842-91e6-4e8b-884f-3aad9480e5b6   \n",
       "4          None    None     None   None  209c0322-3ad0-42a7-a80d-6b15d9a73012   \n",
       "5          None    None     None   None  caac3512-ecac-46ad-9c1a-c213f0711a36   \n",
       "6          None    None     None   None  a917d63d-d3f3-4705-a65e-5f90adeeb0aa   \n",
       "7          None    None     None   None  92d90037-6e61-415d-9e6e-600fe08b5ad8   \n",
       "8          None    None     None   None  471b9152-9cda-4b42-a280-06a0df6a2109   \n",
       "9          None    None     None   None  f91bd4b3-9fc6-4f31-a1a9-c562124782c1   \n",
       "10         None    None     None   None  5e9393aa-b144-41f0-b2bf-7cfcc303c49b   \n",
       "11         None    None     None   None  7cec17bb-0ddb-4f77-b311-8277bdfcd95e   \n",
       "12         None    None     None   None  91c52ea1-dc09-4f0e-bad5-650a5a710c9d   \n",
       "13         None    None     None   None  e05dbc40-07ba-43aa-8b7e-d58eb4abc7c9   \n",
       "\n",
       "    OBJECTID activity_timestamp attribute_0  \\\n",
       "0        112               None        None   \n",
       "1        113               None        None   \n",
       "2        114               None        None   \n",
       "3        115               None        None   \n",
       "4        116               None        None   \n",
       "5        117               None        None   \n",
       "6        118               None        None   \n",
       "7        120               None        None   \n",
       "8        121               None        None   \n",
       "9        122               None        None   \n",
       "10       123               None        None   \n",
       "11       124               None        None   \n",
       "12       125               None        None   \n",
       "13       126               None        None   \n",
       "\n",
       "                                              content createdAt  \\\n",
       "0   https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "1   https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "2   https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "3   https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "4   https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "5   https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "6   https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "7   https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "8   https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "9   https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "10  https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "11  https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "12  https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "13  https://www.cobbcommute.org/CameraProxy/Index?...      None   \n",
       "\n",
       "                        ...                        threshold_val_buses  \\\n",
       "0                       ...                                        200   \n",
       "1                       ...                                        200   \n",
       "2                       ...                                        200   \n",
       "3                       ...                                        200   \n",
       "4                       ...                                        200   \n",
       "5                       ...                                        200   \n",
       "6                       ...                                        200   \n",
       "7                       ...                                        200   \n",
       "8                       ...                                        200   \n",
       "9                       ...                                        200   \n",
       "10                      ...                                        200   \n",
       "11                      ...                                        200   \n",
       "12                      ...                                        200   \n",
       "13                      ...                                        200   \n",
       "\n",
       "   threshold_val_cars threshold_val_ped  threshold_val_trck  thrsh_status_bus  \\\n",
       "0                 200               200                 200             Below   \n",
       "1                 200               200                 200             Below   \n",
       "2                 200               200                 200             Below   \n",
       "3                 200               200                 200             Below   \n",
       "4                 200               200                 200             Below   \n",
       "5                 200               200                 200             Below   \n",
       "6                 200               200                 200             Below   \n",
       "7                 200               200                 200             Below   \n",
       "8                 200               200                 200             Below   \n",
       "9                 200               200                 200             Below   \n",
       "10                200               200                 200             Below   \n",
       "11                200               200                 200             Below   \n",
       "12                200               200                 200             Below   \n",
       "13                200               200                 200             Below   \n",
       "\n",
       "    thrsh_status_cars  thrsh_status_ped  thrsh_status_trck updatedAt  \\\n",
       "0               Below             Below              Below      None   \n",
       "1               Below             Below              Below      None   \n",
       "2               Below             Below              Below      None   \n",
       "3               Below             Below              Below      None   \n",
       "4               Below             Below              Below      None   \n",
       "5               Below             Below              Below      None   \n",
       "6               Below             Below              Below      None   \n",
       "7               Below             Below              Below      None   \n",
       "8               Below             Below              Below      None   \n",
       "9               Below             Below              Below      None   \n",
       "10              Below             Below              Below      None   \n",
       "11              Below             Below              Below      None   \n",
       "12              Below             Below              Below      None   \n",
       "13              Below             Below              Below      None   \n",
       "\n",
       "                                            SHAPE  \n",
       "0    {'x': -9402733.462, 'y': 4014445.4543000013}  \n",
       "1    {'x': -9402693.929, 'y': 4014468.0446000025}  \n",
       "2   {'x': -9402435.5526, 'y': 4014701.0068999976}  \n",
       "3   {'x': -9403301.0543, 'y': 4013961.6180000007}  \n",
       "4          {'x': -9403327.1719, 'y': 4013976.783}  \n",
       "5   {'x': -9403134.5081, 'y': 4013664.1466000006}  \n",
       "6    {'x': -9403081.127, 'y': 4013706.7436000034}  \n",
       "7   {'x': -9403213.4338, 'y': 4013273.2920999974}  \n",
       "8   {'x': -9402963.2101, 'y': 4013196.6242000014}  \n",
       "9   {'x': -9403450.5928, 'y': 4013687.2523000017}  \n",
       "10   {'x': -9403674.4942, 'y': 4013871.311999999}  \n",
       "11   {'x': -9403815.719, 'y': 4014130.0661000013}  \n",
       "12    {'x': -9403157.46, 'y': 4014853.7245000005}  \n",
       "13  {'x': -9405079.1592, 'y': 4015780.6895999983}  \n",
       "\n",
       "[14 rows x 47 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing camera 1 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 2 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 3 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 4 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 5 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 6 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 7 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 8 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 9 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 10 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 11 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 12 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 13 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 14 of 14...\n",
      "Resetting trends...\n",
      "Processing camera 1 of 14...\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-19-262ce72ce1a6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    161\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    162\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[1;32m--> 163\u001b[1;33m                     \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[0m\u001b[0;32m    164\u001b[0m                         \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[0;32m    165\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;36mget_list\u001b[1;34m(self, oid)\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_list\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[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m         \u001b[1;34m\"\"\" returns the list of attachements for a given OBJECT ID \"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\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_list_attachments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'attachmentInfos'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdownload\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[0msave_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\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_list_attachments\u001b[1;34m(self, oid)\u001b[0m\n\u001b[0;32m    202\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    203\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/attachments\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0moid\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 204\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[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\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[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    205\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    206\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;36mget\u001b[1;34m(self, path, params, ssl, compress, try_json, is_retry, use_ordered_dict, out_folder, file_name, force_bytes, add_token, token)\u001b[0m\n\u001b[0;32m    823\u001b[0m             \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handlers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\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[1;32m--> 825\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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    826\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    827\u001b[0m             resp_data, is_file = self._process_response(resp,\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   1316\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1317\u001b[0m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[1;32m-> 1318\u001b[1;33m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\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;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mrequest\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1237\u001b[0m                 encode_chunked=False):\n\u001b[0;32m   1238\u001b[0m         \u001b[1;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1239\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1241\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_send_request\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\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;36m_send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1283\u001b[0m             \u001b[1;31m# default charset of iso-8859-1.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1284\u001b[0m             \u001b[0mbody\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'body'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1285\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1286\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1287\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgetresponse\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;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mendheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1232\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1233\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1234\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1236\u001b[0m     def request(self, method, url, body=None, headers={}, *,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1024\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34mb\"\\r\\n\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1025\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\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[1;32m-> 1026\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1027\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1028\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmessage_body\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;32mC:\\ProgramData\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    962\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    963\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 964\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\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    965\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    966\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mNotConnected\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;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1390\u001b[0m             \u001b[1;34m\"Connect to a host on a given (SSL) port.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1391\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1392\u001b[1;33m             \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\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   1393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1394\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_tunnel_host\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;36mconnect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    934\u001b[0m         \u001b[1;34m\"\"\"Connect to the host and port specified in __init__.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    935\u001b[0m         self.sock = self._create_connection(\n\u001b[1;32m--> 936\u001b[1;33m             (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[0;32m    937\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    938\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address)\u001b[0m\n\u001b[0;32m    711\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0msource_address\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    712\u001b[0m                 \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 713\u001b[1;33m             \u001b[0msock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msa\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    714\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    715\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Establish runtime parameters\n",
    "\n",
    "overwrite_attachment = True\n",
    "upload_source = False\n",
    "write_trends = True\n",
    "reset_trends = True\n",
    "\n",
    "# Start up TF detection graph\n",
    "with detection_graph.as_default():\n",
    "    \n",
    "    # Start up TF session using the detection graph\n",
    "    with tf.Session(graph=detection_graph) as sess:\n",
    "        \n",
    "        # Refer to specific input/output tensors\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",
    "        # Start iteration that determines how many detection sweeps occur\n",
    "        for iteration in range(10):\n",
    "            \n",
    "            # Retrieve the list of cameras from the feature service (as a dataframe)\n",
    "            ddot_df = obj_df\n",
    "            \n",
    "            for ix in range(0,len(obj_df)):\n",
    "                print(\"Processing camera {0} of {1}...\".format(str(ix+1), str(len(obj_df))))\n",
    "\n",
    "                # Retrieve camera information\n",
    "                camera_jpeg_url = obj_df.iloc[ix]['content']\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",
    "                      max_boxes_to_draw=None,\n",
    "                      min_score_thresh=0.30\n",
    "                )\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=\"\"\"content = '\"\"\"+camera_jpeg_url+\"\"\"'\"\"\", 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",
    "                obj_id = feature_to_be_updated.attributes['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": [
    "# TODOs\n",
    "\n",
    "- Change \"Reset Trend Routine\"\n",
    "\n",
    "- Option 1\n",
    "    - Write function to build image training data for labeling\n",
    "    - Retrieve training data for \"crowd blobs\"\n",
    "    - Label images\n",
    "    - Export to PASCAL VOC\n",
    "    - Train model in TF\n",
    "    \n",
    "- Option 2\n",
    "    - Get KITTI model working on static images\n",
    "    - Determine performance\n",
    "    * - This model is not good for people "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TODO\n",
    "\n",
    "o\tWrite python script to generate unlabeled images from Cobb County feed – In progress\n",
    "\n",
    "o\tSchedule script to execute on:\n",
    "\n",
    "    \tEvent (daytime) - Pending\n",
    "\n",
    "    \tNon-event (daytime) - Pending\n",
    "\n",
    "    \tEvent (night-time) – Pending  (Optional)\n",
    "\n",
    "    \tNon-event (night-time) – Pending  (Optional)\n",
    "\n",
    "o\tTraining Data Labeling – Pending\n",
    "\n",
    "o\tModel Training – Pending\n",
    "\n",
    "o\tModel Testing – Pending\n",
    "\n",
    "o\tFront-end Config - Pending"
   ]
  },
  {
   "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,len(obj_df)):\n",
    "        print(\"Resetting values for camera {0} of {1}...\".format(str(ix), len(obj_df)))\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)"
   ]
  }
 ],
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