QSkipDetection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V8-yl-s-WKMG",
    "colab_type": "text"
   },
   "source": [
    "# Object Detection Demo\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/research/object_detection/g3doc/installation.md) before you start."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kFSqkTCdWKMI",
    "colab_type": "text"
   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "ekQSqcjN5jX1",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "#!git clone https://github.com/tensorflow/models.git\n",
    "#!apt-get install protobuf-compiler python-pil python-lxml python-tk\n",
    "# import os\n",
    "# os.chdir('models/research/')\n",
    "#!protoc object_detection/protos/*.proto --python_out=.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "vlnTKItt5ziw",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "hV4P5gyTWKMI",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "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",
    "\n",
    "import zipfile\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",
    "# This is needed since the notebook is stored in the object_detection folder.\n",
    "sys.path.append(\"..\")\n",
    "from object_detection.utils import ops as utils_ops\n",
    "\n",
    "if tf.__version__ < '1.4.0':\n",
    "  print(tf.__version__)\n",
    "  raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wy72mWwAWKMK",
    "colab_type": "text"
   },
   "source": [
    "## Env setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "v7m_NY_aWKMK",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "# This is needed to display the images.\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "r5FNuiRPWKMN",
    "colab_type": "text"
   },
   "source": [
    "## Object detection imports\n",
    "Here are the imports from the object detection module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "bm0_uNRnWKMN",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     },
     "base_uri": "https://localhost:8080/",
     "height": 1290.0
    },
    "outputId": "ae8d1845-f151-42f5-a4aa-bfc1085eb17a",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.53320115775E12,
     "user_tz": -330.0,
     "elapsed": 1340.0,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     }
    },
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/uniquetrij/Projects/SecureIt/object_detection/utils/visualization_utils.py:25: UserWarning: \nThis call to matplotlib.use() has no effect because the backend has already\nbeen chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\nor matplotlib.backends is imported for the first time.\n\nThe backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/traitlets/config/application.py\", line 658, in launch_instance\n    app.start()\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/ipykernel/kernelapp.py\", line 486, in start\n    self.io_loop.start()\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 132, in start\n    self.asyncio_loop.run_forever()\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/asyncio/base_events.py\", line 422, in run_forever\n    self._run_once()\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/asyncio/base_events.py\", line 1434, in _run_once\n    handle._run()\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/asyncio/events.py\", line 145, in _run\n    self._callback(*self._args)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/tornado/platform/asyncio.py\", line 122, in _handle_events\n    handler_func(fileobj, events)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 450, in _handle_events\n    self._handle_recv()\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 480, in _handle_recv\n    self._run_callback(callback, msg)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py\", line 432, in _run_callback\n    callback(*args, **kwargs)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 233, in dispatch_shell\n    handler(stream, idents, msg)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\n    user_expressions, allow_stdin)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/ipykernel/ipkernel.py\", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/ipykernel/zmqshell.py\", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2705, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2815, in run_ast_nodes\n    if self.run_code(code, result):\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2869, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-2-5a31747f16e3>\", line 2, in <module>\n    get_ipython().magic('matplotlib inline')\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2146, in magic\n    return self.run_line_magic(magic_name, magic_arg_s)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2067, in run_line_magic\n    result = fn(*args,**kwargs)\n  File \"<decorator-gen-104>\", line 2, in matplotlib\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/IPython/core/magic.py\", line 188, in <lambda>\n    call = lambda f, *a, **k: f(*a, **k)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/IPython/core/magics/pylab.py\", line 100, in matplotlib\n    gui, backend = self.shell.enable_matplotlib(args.gui)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/IPython/core/interactiveshell.py\", line 2935, in enable_matplotlib\n    pt.activate_matplotlib(backend)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/IPython/core/pylabtools.py\", line 296, in activate_matplotlib\n    matplotlib.pyplot.switch_backend(backend)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/matplotlib/pyplot.py\", line 233, in switch_backend\n    _backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/matplotlib/backends/__init__.py\", line 62, in pylab_setup\n    [backend_name], 0)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\", line 165, in <module>\n    _enable_matplotlib_integration()\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/ipykernel/pylab/backend_inline.py\", line 155, in _enable_matplotlib_integration\n    activate_matplotlib(backend)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/IPython/core/pylabtools.py\", line 296, in activate_matplotlib\n    matplotlib.pyplot.switch_backend(backend)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/matplotlib/pyplot.py\", line 231, in switch_backend\n    matplotlib.use(newbackend, warn=False, force=True)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/matplotlib/__init__.py\", line 1422, in use\n    reload(sys.modules['matplotlib.backends'])\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/importlib/__init__.py\", line 166, in reload\n    _bootstrap._exec(spec, module)\n  File \"/usr/local/anaconda3/envs/SecureIt/lib/python3.6/site-packages/matplotlib/backends/__init__.py\", line 16, in <module>\n    line for line in traceback.format_stack()\n\n\n  import matplotlib; matplotlib.use('Agg')  # pylint: disable=multiple-statements\n"
     ]
    }
   ],
   "source": [
    "from object_detection.utils import label_map_util\n",
    "\n",
    "from object_detection.utils import visualization_utils as vis_util"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cfn_tRFOWKMO",
    "colab_type": "text"
   },
   "source": [
    "# Model preparation "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X_sEBLpVWKMQ",
    "colab_type": "text"
   },
   "source": [
    "## Variables\n",
    "\n",
    "Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_FROZEN_GRAPH` 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/research/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": "code",
   "execution_count": 4,
   "metadata": {
    "id": "VyPz_t8WWKMQ",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "# What model to download.\n",
    "MODEL_PATH = \"object_detection/pretrained/\"\n",
    "#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'  # SSD Mobilenet\n",
    "MODEL_NAME = 'faster_rcnn_inception_v2_coco_2018_01_28' # Faster RCNN \n",
    "\n",
    "MODEL_FILE = MODEL_PATH + 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_FROZEN_GRAPH = MODEL_PATH + 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('object_detection/data', 'mscoco_label_map.pbtxt')\n",
    "\n",
    "NUM_CLASSES = 90"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7ai8pLZZWKMS",
    "colab_type": "text"
   },
   "source": [
    "## Download Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "KILYnwR5WKMS",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    },
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "HTTPError",
     "evalue": "HTTP Error 403: Forbidden",
     "traceback": [
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      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
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      "\u001b[0;32m/usr/local/anaconda3/envs/SecureIt/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self, url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m   1789\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1790\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1791\u001b[0;31m         \u001b[0mfp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1792\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1793\u001b[0m             \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/envs/SecureIt/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data)\u001b[0m\n\u001b[1;32m   1755\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1756\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1757\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1758\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1759\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/envs/SecureIt/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mopen_http\u001b[0;34m(self, url, data)\u001b[0m\n\u001b[1;32m   1934\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mopen_http\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1935\u001b[0m         \u001b[0;34m\"\"\"Use HTTP protocol.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1936\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_generic_http\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1938\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhttp_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/envs/SecureIt/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36m_open_generic_http\u001b[0;34m(self, connection_factory, url, data)\u001b[0m\n\u001b[1;32m   1930\u001b[0m             return self.http_error(\n\u001b[1;32m   1931\u001b[0m                 \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1932\u001b[0;31m                 response.status, response.reason, response.msg, data)\n\u001b[0m\u001b[1;32m   1933\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1934\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mopen_http\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/envs/SecureIt/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mhttp_error\u001b[0;34m(self, url, fp, errcode, errmsg, headers, data)\u001b[0m\n\u001b[1;32m   1950\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1951\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1952\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhttp_error_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1953\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1954\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhttp_error_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/envs/SecureIt/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mhttp_error_default\u001b[0;34m(self, url, fp, errcode, errmsg, headers)\u001b[0m\n\u001b[1;32m   1955\u001b[0m         \u001b[0;34m\"\"\"Default error handler: close the connection and raise OSError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1956\u001b[0m         \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1957\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1958\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1959\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_have_ssl\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mHTTPError\u001b[0m: HTTP Error 403: Forbidden"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "opener = urllib.request.URLopener()\n",
    "opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)\n",
    "tar_file = tarfile.open(MODEL_FILE)\n",
    "for file in tar_file.getmembers():\n",
    "  file_name = os.path.basename(file.name)\n",
    "  if 'frozen_inference_graph.pb' in file_name:\n",
    "    tar_file.extract(file, os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YBcB9QHLWKMU",
    "colab_type": "text"
   },
   "source": [
    "## Load a (frozen) Tensorflow model into memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "KezjCRVvWKMV",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "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_FROZEN_GRAPH, '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": {
    "id": "_1MVVTcLWKMW",
    "colab_type": "text"
   },
   "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": 13,
   "metadata": {
    "id": "hDbpHkiWWKMX",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "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": {
    "id": "EFsoUHvbWKMZ",
    "colab_type": "text"
   },
   "source": [
    "## Helper code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "aSlYc3JkWKMa",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "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": {
    "id": "H0_1AGhrWKMc",
    "colab_type": "text"
   },
   "source": [
    "# Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "jG-zn5ykWKMd",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "# For the sake of simplicity we will use only 2 images:\n",
    "# image1.jpg\n",
    "# image2.jpg\n",
    "# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.\n",
    "PATH_TO_TEST_IMAGES_DIR = 'object_detection/test_images'\n",
    "TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 1000) ]\n",
    "\n",
    "# Size, in inches, of the output images.\n",
    "IMAGE_SIZE = (12, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "92BHxzcNWKMf",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "def run_inference_for_single_image(image, graph):\n",
    "  with graph.as_default():\n",
    "    with tf.Session() as sess:\n",
    "      # Get handles to input and output tensors\n",
    "      ops = tf.get_default_graph().get_operations()\n",
    "      all_tensor_names = {output.name for op in ops for output in op.outputs}\n",
    "      tensor_dict = {}\n",
    "      for key in [\n",
    "          'num_detections', 'detection_boxes', 'detection_scores',\n",
    "          'detection_classes', 'detection_masks'\n",
    "      ]:\n",
    "        tensor_name = key + ':0'\n",
    "        if tensor_name in all_tensor_names:\n",
    "          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(\n",
    "              tensor_name)\n",
    "      if 'detection_masks' in tensor_dict:\n",
    "        # The following processing is only for single image\n",
    "        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])\n",
    "        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])\n",
    "        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.\n",
    "        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)\n",
    "        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])\n",
    "        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])\n",
    "        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n",
    "            detection_masks, detection_boxes, image.shape[0], image.shape[1])\n",
    "        detection_masks_reframed = tf.cast(\n",
    "            tf.greater(detection_masks_reframed, 0.5), tf.uint8)\n",
    "        # Follow the convention by adding back the batch dimension\n",
    "        tensor_dict['detection_masks'] = tf.expand_dims(\n",
    "            detection_masks_reframed, 0)\n",
    "      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')\n",
    "\n",
    "      # Run inference\n",
    "      output_dict = sess.run(tensor_dict,\n",
    "                             feed_dict={image_tensor: np.expand_dims(image, 0)})\n",
    "\n",
    "      # all outputs are float32 numpy arrays, so convert types as appropriate\n",
    "      output_dict['num_detections'] = int(output_dict['num_detections'][0])\n",
    "      output_dict['detection_classes'] = output_dict[\n",
    "          'detection_classes'][0].astype(np.uint8)\n",
    "      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]\n",
    "      output_dict['detection_scores'] = output_dict['detection_scores'][0]\n",
    "      if 'detection_masks' in output_dict:\n",
    "        output_dict['detection_masks'] = output_dict['detection_masks'][0]\n",
    "  return output_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "3a5wMHN8WKMh",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     },
     "base_uri": "https://localhost:8080/",
     "height": 2031.0
    },
    "outputId": "758869a1-5cc2-48d9-d358-ed498fb5c15c",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.533201845597E12,
     "user_tz": -330.0,
     "elapsed": 25639.0,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     }
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'TEST_IMAGE_PATHS' is not defined",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-e20511a04c09>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mimage_path\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mTEST_IMAGE_PATHS\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'TEST_IMAGE_PATHS' is not defined"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "for image_path in TEST_IMAGE_PATHS:\n",
    "  try:\n",
    "    image = Image.open(image_path)\n",
    "  except:\n",
    "    continue\n",
    "  # the array based representation of the image will be used later in order to prepare the\n",
    "  # result image with boxes and labels on it.\n",
    "  image_np = load_image_into_numpy_array(image)\n",
    "  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
    "  image_np_expanded = np.expand_dims(image_np, axis=0)\n",
    "  # Actual detection.\n",
    "  output_dict = run_inference_for_single_image(image_np, detection_graph)\n",
    "  # Visualization of the results of a detection.\n",
    "  vis_util.visualize_boxes_and_labels_on_image_array(\n",
    "      image_np,\n",
    "      output_dict['detection_boxes'],\n",
    "      output_dict['detection_classes'],\n",
    "      output_dict['detection_scores'],\n",
    "      category_index,\n",
    "      instance_masks=output_dict.get('detection_masks'),\n",
    "      use_normalized_coordinates=True,\n",
    "      line_thickness=8)\n",
    "  plt.figure(figsize=IMAGE_SIZE)\n",
    "  plt.imshow(image_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "LQSEnEsPWKMj",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "def visualiz(img, detection_boxes, detection_classes, detection_scores):\n",
    "  height, width = img.shape[0], img.shape[1]\n",
    "  region = [[264., 355.],\n",
    "      [369., 202.],\n",
    "      [600., 202.],\n",
    "      [600., 355.]]\n",
    "  bbPath = mplPath.Path(\n",
    "      np.array(region))\n",
    "  red = (255, 0, 0)\n",
    "  green = (0, 255, 0)  \n",
    "  for i in range(len(detection_boxes)):\n",
    "#       print(i)\n",
    "    if detection_classes[i] == 1 and detection_scores[i] > 0.3 :\n",
    "      y_tl, x_tl, y_br, x_br = detection_boxes[i]*[height, width, height, width]\n",
    "      path = [[x_tl, y_tl],\n",
    "             [x_tl, y_br],\n",
    "             [x_br, y_br],\n",
    "             [x_br, y_tl]]\n",
    "      \n",
    "      color = green      \n",
    "      if bbPath.contains_point(((x_br+x_tl)/2, y_br)):\n",
    "        color = red\n",
    "      \n",
    "      img = cv2.polylines(img, [np.int32(path)], 1, color, 3)\n",
    "#   plt.figure(figsize=IMAGE_SIZE)\n",
    "#   plt.imshow(img)\n",
    "#   plt.show()\n",
    "  return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "rQa4WCOB7Hum",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.path as mplPath\n",
    "import numpy as np\n",
    "\n",
    "for image_path in TEST_IMAGE_PATHS:\n",
    "  #image = Image.open(image_path)\n",
    "  import cv2\n",
    "  image = cv2.imread(image_path)\n",
    "#   print(type(image))\n",
    "  # the array based representation of the image will be used later in order to prepare the\n",
    "  # result image with boxes and labels on it.\n",
    "  if image is None:\n",
    "    continue\n",
    "  height, width = image.shape[0], image.shape[1]\n",
    "  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
    "  image_np_expanded = np.expand_dims(image, axis=0)\n",
    "  # Actual detection.\n",
    "  output_dict = run_inference_for_single_image(image, detection_graph)\n",
    "  # Visualization of the results of a detection.\n",
    "  \n",
    "  visualiz(image,output_dict['detection_boxes'],output_dict['detection_classes'],output_dict['detection_scores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "TFidwRaZ7Lk7",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "import os \n",
    "os.chdir('object_detection/test_images/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "MKy9QsDi8FOk",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    ""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "-4xExcfg7Sg6",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     },
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "ok": true,
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "status": 200.0,
       "status_text": ""
      }
     },
     "base_uri": "https://localhost:8080/",
     "height": 91.0
    },
    "outputId": "12378388-547e-413e-d43d-8b6ba3ed3268",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.533202636111E12,
     "user_tz": -330.0,
     "elapsed": 63954.0,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-31cb4615-7f91-44f1-b2a6-459852a49f14\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-31cb4615-7f91-44f1-b2a6-459852a49f14\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving VID-20180801-WA0004.mp4 to VID-20180801-WA0004.mp4\n",
      "User uploaded file \"VID-20180801-WA0004.mp4\" with length 3873569 bytes\n"
     ]
    }
   ],
   "source": [
    "# from google.colab import files\n",
    "# \n",
    "# uploaded = files.upload()\n",
    "# \n",
    "# for fn in uploaded.keys():\n",
    "#   print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
    "#       name=fn, length=len(uploaded[fn])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "FLqqT3Mo8Gzg",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "os.chdir('../..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "HiZpjAFV_YYt",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "#!pip install moviepy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "TOPXwjNy_GgZ",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "from moviepy.editor import VideoFileClip\n",
    "from IPython.display import HTML\n",
    "def process_image(image):\n",
    "    height, width = image.shape[0], image.shape[1]\n",
    "  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
    "    image_np_expanded = np.expand_dims(image, axis=0)\n",
    "  # Actual detection.\n",
    "    output_dict = run_inference_for_single_image(image, detection_graph)\n",
    "#     print(output_dict['detection_boxes'])\n",
    "    image = visualiz(image,output_dict['detection_boxes'],output_dict['detection_classes'],output_dict['detection_scores'])\n",
    "    \n",
    "    return image\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "pYwxgpe5_j3o",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     },
     "base_uri": "https://localhost:8080/",
     "height": 2108.0
    },
    "outputId": "facfbb90-847b-48eb-b8d2-3897cd67f0f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] >>>> Building video object_detection/test_images/output.mp4\n",
      "[MoviePy] Writing video object_detection/test_images/output.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stderr",
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     ]
    }
   ],
   "source": [
    "white_output = 'object_detection/test_images/output.mp4'\n",
    "clip1 = VideoFileClip(\"object_detection/test_images/VID-20180801-WA0004.mp4\")#.subclip(0,2)\n",
    "white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!s\n",
    "%time white_clip.write_videofile(white_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "id": "bFtp1Iw6BZnh",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     },
     "resources": {
      "http://localhost:8080/object_detection/test_images/output.mp4": {
       "data": "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",
       "ok": false,
       "headers": [
        [
         "content-length",
         "1449"
        ],
        [
         "content-type",
         "text/html; charset=utf-8"
        ]
       ],
       "status": 404.0,
       "status_text": ""
      }
     },
     "base_uri": "https://localhost:8080/",
     "height": 561.0
    },
    "outputId": "7bd8e290-0994-433a-bdc5-acddb0d59f9c",
    "executionInfo": {
     "status": "ok",
     "timestamp": 1.533203052105E12,
     "user_tz": -330.0,
     "elapsed": 948.0,
     "user": {
      "displayName": "",
      "photoUrl": "",
      "userId": ""
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<video width=\"960\" height=\"540\" controls>\n",
       "  <source src=\"object_detection/test_images/output.mp4\">\n",
       "</video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 71,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(\"\"\"\n",
    "<video width=\"960\" height=\"540\" controls>\n",
    "  <source src=\"{0}\">\n",
    "</video>\n",
    "\"\"\".format(white_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "id": "3_LPz7P4BmTq",
    "colab_type": "code",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0.0
     }
    }
   },
   "outputs": [],
   "source": [
    "# from google.colab import files\n",
    "# \n",
    "# \n",
    "# files.download('object_detection/test_images/output.mp4')"
   ]
  }
 ],
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