02-open-images-data-manipulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Open Images data preparation V2\n",
    "\n",
    "Copy of V1 notebook (01) (refining data preparation code even further).\n",
    "\n",
    "The goal of this notebook is to get the public Open Images data formatted in such a way it can be used with Detectron2.\n",
    "\n",
    "To download the Open Images, we're using the following script (modified):\n",
    "- https://github.com/spmallick/learnopencv/blob/master/downloadOpenImages/downloadOI.py\n",
    "\n",
    "We need to format the images downloaded using the script into Detectron2 style:\n",
    "- https://detectron2.readthedocs.io/tutorials/datasets.html\n",
    "\n",
    "Detectron2 example style:\n",
    "```\n",
    "{'annotations': [{'bbox': [994, 619, 1445, 1166],\n",
    "   'bbox_mode': <BoxMode.XYXY_ABS: 0>,\n",
    "   'category_id': 0,\n",
    "   'iscrowd': 0,\n",
    "   'segmentation': [[1020.5,\n",
    "     963.5,\n",
    "     1000.5,\n",
    "     841.5,\n",
    "\t\t ... #### Truncated ####\n",
    "     989.5,\n",
    "     1020.5,\n",
    "     963.5]]}],\n",
    " 'file_name': 'balloon/train/34020010494_e5cb88e1c4_k.jpg',\n",
    " 'height': 1536,\n",
    " 'image_id': 0,\n",
    " 'width': 2048}\n",
    "```\n",
    "\n",
    "An example notebook using Detectron2 style inputs can be found here: https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5\n",
    "\n",
    "We'll start by downloading a few sample images, then build a little script which converts the samples to Detectron2 style before ramping it up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Install wget\n",
    "# import sys\n",
    "# !conda install --yes --prefix {sys.prefix} -c anaconda wget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Install OpenCV (cv2)\n",
    "# import sys\n",
    "# !conda install --yes --prefix {sys.prefix} -c conda-forge opencv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install tqdm & awscli\n",
    "#!conda install --yes --prefix {sys.prefix} -c conda-forge tqdm awscli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Download required meta-files for V5 - https://storage.googleapis.com/openimages/web/download.html\n",
    "\n",
    "# # Training bounding boxes (1.11G)\n",
    "# !wget https://storage.googleapis.com/openimages/2018_04/train/train-annotations-bbox.csv\n",
    "\n",
    "# # Validating bounding boxes (23.94M)\n",
    "# !wget https://storage.googleapis.com/openimages/v5/validation-annotations-bbox.csv\n",
    "    \n",
    "# # Testing bounding boxes (73.89M)\n",
    "# !wget https://storage.googleapis.com/openimages/v5/test-annotations-bbox.csv\n",
    "\n",
    "# # Class Names (11.73K)\n",
    "# !wget https://storage.googleapis.com/openimages/v5/class-descriptions-boxable.csv\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classes of amenities Airbnb mostly cares about\n",
    "subset = [\"Toilet\",\n",
    "          \"Swimming_pool\",\n",
    "          \"Bed\",\n",
    "          \"Billiard_table\",\n",
    "          \"Sink\",\n",
    "          \"Fountain\",\n",
    "          \"Oven\",\n",
    "          \"Ceiling_fan\",\n",
    "          \"Television\",\n",
    "          \"Microwave_oven\",\n",
    "          \"Gas_stove\",\n",
    "          \"Refrigerator\",\n",
    "          \"Kitchen_&_dining_room_table\",\n",
    "          \"Washing_machine\",\n",
    "          \"Bathtub\",\n",
    "          \"Stairs\",\n",
    "          \"Fireplace\",\n",
    "          \"Pillow\",\n",
    "          \"Mirror\",\n",
    "          \"Shower\",\n",
    "          \"Couch\",\n",
    "          \"Countertop\",\n",
    "          \"Coffeemaker\",\n",
    "          \"Dishwasher\",\n",
    "          \"Sofa_bed\",\n",
    "          \"Tree_house\",\n",
    "          \"Towel\",\n",
    "          \"Porch\",\n",
    "          \"Wine_rack\",\n",
    "          \"Jacuzzi\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 0 : Bed\n",
      "Class annotations 1: 03646bb2f288eaae,xclick,/m/03ssj5,1,0,0.9985229,0.22295806,0.9977925,1,1,0,0,0\n",
      "0a6bc386b28f2aac,xclick,/m/03ssj5,1,0.182,0.716,0.536,0.928,0,0,0,0,0\n",
      "0a6bc386b28f2aac,xclick,/m/03ssj5,1,0.376,0.99,0.59466666,1,0,0,0,0,0\n",
      "0fb3a760f909c8c9,xclick,/m/03ssj5,1,0.28541666,0.99791664,0.48043817,0.9984351,1,1,0,0,0\n",
      "1229d5e203956ac6,xclick,/m/03ssj5,1,0,0.3609375,0.18333334,0.775,0,1,0,0,0\n",
      "1229d5e203956ac6,xclick,/m/03ssj5,1,0.63125,1,0.26875,0.9791667,0,0,0,0,0\n",
      "15e9f230f8f0609f,xclick,/m/03ssj5,1,0,1,0.70625,1,0,1,0,0,0\n",
      "17e05469444c8e0a,xclick,/m/03ssj5,1,0,1,0.21666667,1,0,1,0,0,0\n",
      "18c8303c966a5ac6,xclick,/m/03ssj5,1,0,1,0.017699115,1,0,1,0,0,0\n",
      "18ced18aee04c48e,xclick,/m/03ssj5,1,0,1,0,1,0,1,0,0,0\n",
      "1d5bb9d9a8a49e84,xclick,/m/03ssj5,1,0,0.8421875,0.079166666,1,0,0,0,0,0\n",
      "208e95b8f33fecda,xclick,/m/03ssj5,1,0,0.6233383,0.5629139,1,0,1,0,0,0\n",
      "20af1d21449acdce,xclick,/m/03ssj5,1,0,0.4640625,0,0.48958334,1,1,0,0,0\n",
      "2110e5d8ca7e5a99,xclick,/m/03ssj5,1,0,0.9984375,0,0.99791664,1,1,0,0,0\n",
      "288150fcf6fcac88,xclick,/m/03ssj5,1,0.396875,1,0.3125,1,1,1,0,0,0\n",
      "28a4c2b31f945e5f,xclick,/m/03ssj5,1,0.5097561,0.997561,0.5667276,0.9140768,1,0,0,0,0\n",
      "2ac96d4eed4e33c8,xclick,/m/03ssj5,1,0.3578125,0.9984375,0.0020833334,0.66041666,0,1,0,0,0\n",
      "2ed000d799794d71,xclick,/m/03ssj5,1,0,1,0.38412017,1,0,1,0,0,0\n",
      "36fb4da1df30f769,xclick,/m/03ssj5,1,0,0.9983361,0,0.99607843,1,1,0,0,0\n",
      "38f481601bac0d4a,xclick,/m/03ssj5,1,0,0.69264704,0.3436807,1,1,1,0,0,0\n",
      "38f481601bac0d4a,xclick,/m/03ssj5,1,0.4779412,0.9044118,0.2949002,0.6895787,1,0,0,0,0\n",
      "3a689e83e0e9f6e9,xclick,/m/03ssj5,1,0.39583334,1,0.6125,1,0,0,0,0,0\n",
      "3bbc38b3beafe343,xclick,/m/03ssj5,1,0,0.7796875,0.38333333,0.99791664,0,1,0,0,0\n",
      "3f65d9a5db068b25,xclick,/m/03ssj5,1,0.0015625,0.859375,0.14583333,1,0,1,0,0,0\n",
      "3fb5e100e9962553,xclick,/m/03ssj5,1,0.39290586,1,0.52870816,1,0,0,0,0,0\n",
      "408bfc9875660552,xclick,/m/03ssj5,1,0.15408805,0.7279874,0.6141079,1,0,0,0,0,0\n",
      "409019c958741906,xclick,/m/03ssj5,1,0.15511551,0.6732673,0.54347825,0.96837944,0,0,0,0,0\n",
      "4319a313fae699f9,xclick,/m/03ssj5,1,0,0.41875,0,1,0,1,0,0,0\n",
      "4319a313fae699f9,xclick,/m/03ssj5,1,0.3015625,0.5453125,0.054166667,0.97291666,1,0,0,0,0\n",
      "4319a313fae699f9,xclick,/m/03ssj5,1,0.4671875,1,0.0375,1,0,1,0,0,0\n",
      "44e6ff6555a759f3,xclick,/m/03ssj5,1,0.08915663,0.97590363,0.38700947,0.65764546,0,0,0,0,0\n",
      "4775ce4f3625482e,xclick,/m/03ssj5,1,0,0.91041666,0.63055557,1,0,1,0,0,0\n",
      "477926ac3bcfb06e,xclick,/m/03ssj5,1,0.225,0.3671875,0.46041667,0.72083336,1,0,0,0,0\n",
      "50123869e3d723f1,xclick,/m/03ssj5,1,0,1,0.033333335,1,0,1,0,0,0\n",
      "50c91362e9bc580f,xclick,/m/03ssj5,1,0,1,0.016666668,1,0,1,0,0,1\n",
      "5991aecc161bb5c8,xclick,/m/03ssj5,1,0,0.9955257,0.24489796,0.8206997,1,1,0,0,0\n",
      "5a44d26db9ea0aa8,xclick,/m/03ssj5,1,0,0.99785864,0.12861736,0.9983923,0,1,0,0,0\n",
      "5b906e188aac9f24,xclick,/m/03ssj5,1,0,0.77262694,0.42772862,0.53687316,0,1,0,0,0\n",
      "5b906e188aac9f24,xclick,/m/03ssj5,1,0,0.777042,0.50884956,0.7699115,0,1,0,0,0\n",
      "5e360c07411fb805,xclick,/m/03ssj5,1,0,0.8640625,0.45,0.99791664,1,0,0,0,0\n",
      "5ebc104d9d7f872c,xclick,/m/03ssj5,1,0,1,0.004166667,0.99375,0,0,0,0,0\n",
      "608fda8c976e0ac5,xclick,/m/03ssj5,1,0.028125,0.778125,0.31458333,1,1,1,0,0,0\n",
      "608fda8c976e0ac5,xclick,/m/03ssj5,1,0.3,0.9515625,0.27916667,0.95,1,0,0,0,0\n",
      "60fdb7fd333eaf74,xclick,/m/03ssj5,1,0.12195122,0.9376694,0.14457831,0.97590363,0,0,0,0,0\n",
      "618dbeb8fb32291f,xclick,/m/03ssj5,1,0,1,0,1,0,1,0,0,0\n",
      "63aae2fcaccefba7,xclick,/m/03ssj5,1,0.042424243,1,0.5573506,1,0,1,0,0,0\n",
      "65a40656a6ba5344,xclick,/m/03ssj5,1,0,0.9985251,0.044247787,0.9977876,1,1,0,0,0\n",
      "666a614b3123dff9,xclick,/m/03ssj5,1,0,0.60625,0.41041666,0.99791664,1,0,0,0,0\n",
      "666a614b3123dff9,xclick,/m/03ssj5,1,0.4890625,0.9984375,0.37291667,1,1,0,0,0,0\n",
      "68907defc0c961e1,xclick,/m/03ssj5,1,0,0.6125,0.44375,1,0,0,0,0,0\n",
      "6c582f286b611dbb,xclick,/m/03ssj5,1,0,1,0.2,1,0,1,0,0,0\n",
      "70a82a2501178f8b,xclick,/m/03ssj5,1,0,0.5,0.36041668,0.99791664,0,1,0,0,0\n",
      "70a82a2501178f8b,xclick,/m/03ssj5,1,0.5875,0.9890625,0.33541667,0.99791664,1,1,0,0,0\n",
      "711f4bda960e3983,xclick,/m/03ssj5,1,0,0.9977876,0.23451327,1,0,0,0,0,0\n",
      "74c9a0c1ab44e47f,xclick,/m/03ssj5,1,0,1,0.5469314,1,0,0,0,0,0\n",
      "755164cc12b2a690,xclick,/m/03ssj5,1,0,0.9640625,0.34791666,1,0,0,0,0,0\n",
      "76c9df7686da333c,xclick,/m/03ssj5,1,0,0.998,0.3233831,0.99751246,0,1,0,0,0\n",
      "7c125815652e330a,xclick,/m/03ssj5,1,0,1,0.11283186,1,0,0,0,0,0\n",
      "7c3b876233adde13,xclick,/m/03ssj5,1,0,1,0.13053097,1,0,0,0,0,0\n",
      "7d97e842bb3c1363,xclick,/m/03ssj5,1,0.01875,0.9890625,0.00625,0.99791664,0,0,0,0,0\n",
      "87b1fa47c3ed5a92,xclick,/m/03ssj5,1,0.19411765,0.6602941,0.4767184,1,0,0,0,0,0\n",
      "898890757a38a6db,xclick,/m/03ssj5,1,0.27794117,0.68088233,0.37915742,0.8203991,0,0,0,1,0\n",
      "89ae6cceba0c6dc7,xclick,/m/03ssj5,1,0,0.8685446,0.40416667,0.99583334,1,1,0,0,0\n",
      "8a01d74cbf72e782,xclick,/m/03ssj5,1,0.2109375,0.909375,0.6125,1,0,1,0,0,0\n",
      "8afd2f0e67254343,xclick,/m/03ssj5,1,0.5524257,1,0.058333334,0.675,1,0,0,0,0\n",
      "8d51d058a5f21775,xclick,/m/03ssj5,1,0.0015625,1,0.027083334,1,0,1,0,0,0\n",
      "9295ab265fafd8e5,xclick,/m/03ssj5,1,0,0.96875,0.114583336,0.99791664,1,1,0,0,0\n",
      "93641625fc063e6c,xclick,/m/03ssj5,1,0,0.6696165,0,1,0,1,0,0,0\n",
      "968d0022f4b1d0dc,xclick,/m/03ssj5,1,0.49103138,0.84753364,0.5334302,1,0,0,0,0,0\n",
      "96b573942e14da60,xclick,/m/03ssj5,1,0.36283186,1,0,1,0,1,0,0,0\n",
      "96c539fa081cd474,xclick,/m/03ssj5,1,0,0.63274336,0,1,0,1,0,0,0\n",
      "96c539fa081cd474,xclick,/m/03ssj5,1,0.71091443,0.9262537,0.17699115,1,1,1,0,0,0\n",
      "980fb374c3ec75e3,xclick,/m/03ssj5,1,0.44035345,0.78350514,0.3761062,0.7234513,0,0,0,0,0\n",
      "994385c9f33f2075,xclick,/m/03ssj5,1,0.0265625,0.9859375,0.73125,0.99791664,1,1,0,0,0\n",
      "994385c9f33f2075,xclick,/m/03ssj5,1,0.0921875,0.359375,0.56041664,0.75,1,0,0,0,0\n",
      "9afab31afacbba12,xclick,/m/03ssj5,1,0.79583335,0.94375,0.3828125,0.64375,1,1,0,0,0\n",
      "9afab31afacbba12,xclick,/m/03ssj5,1,0.86875,1,0.334375,0.834375,0,1,0,0,0\n",
      "a0db623468bab8d5,xclick,/m/03ssj5,1,0.203125,0.7546875,0.31041667,0.91875,0,0,0,0,0\n",
      "a359ca53d4609faa,xclick,/m/03ssj5,1,0,0.996875,0.49583334,1,0,0,0,0,0\n",
      "a5d3b9952ef78c2e,xclick,/m/03ssj5,1,0.5046875,1,0.41041666,1,0,0,0,0,0\n",
      "a86c99772420c77e,xclick,/m/03ssj5,1,0.30686405,0.66890985,0.43583536,0.8958838,0,0,0,0,0\n",
      "a92113b2150cbc53,xclick,/m/03ssj5,1,0.057902973,0.90766823,0.022916667,1,0,0,0,0,0\n",
      "aa41ff683e2c5a60,xclick,/m/03ssj5,1,0.0020833334,0.64166665,0.275,1,0,1,0,0,0\n",
      "ab9b1e4579fd710b,xclick,/m/03ssj5,1,0.15151516,0.92424244,0.4631579,0.99473685,1,1,0,0,0\n",
      "b5f7b2aa13abe836,xclick,/m/03ssj5,1,0,0.8592058,0.2797834,0.99458486,1,0,0,0,0\n",
      "b613c149d79da0d2,xclick,/m/03ssj5,1,0,0.4234375,0.40416667,1,0,1,0,0,0\n",
      "b613c149d79da0d2,xclick,/m/03ssj5,1,0.2296875,0.8171875,0.35833332,0.98541665,0,0,0,0,0\n",
      "b613c149d79da0d2,xclick,/m/03ssj5,1,0.584375,1,0.33125,0.71875,0,0,0,0,0\n",
      "b6f26c9d7c6667c5,xclick,/m/03ssj5,1,0,1,0.20625,1,0,0,0,0,0\n",
      "b833ff7e8c121fd4,xclick,/m/03ssj5,1,0.3109375,0.95625,0.37916666,1,0,0,0,0,0\n",
      "bce01b280e7be3c9,xclick,/m/03ssj5,1,0,0.80625,0.54791665,1,1,1,0,0,0\n",
      "c08da8484c808dbe,xclick,/m/03ssj5,1,0.6386431,0.7787611,0.5685841,0.63274336,0,0,0,0,0\n",
      "c2b9ea7fa6acb336,xclick,/m/03ssj5,1,0.07782672,0.4743025,0.6607539,1,0,0,0,0,0\n",
      "c2b9ea7fa6acb336,xclick,/m/03ssj5,1,0.40381792,0.5315712,0.44124168,0.6696231,1,0,0,0,0\n",
      "c2ed6ddf764b96d4,xclick,/m/03ssj5,1,0.121875,1,0.65,1,0,1,0,0,0\n",
      "c3287d9a3da51af3,xclick,/m/03ssj5,1,0.18611522,1,0.50110376,1,0,1,0,0,0\n",
      "c4f2aabceda78470,xclick,/m/03ssj5,1,0,1,0.1768953,1,0,0,0,0,0\n",
      "c508797245ca5106,xclick,/m/03ssj5,1,0.18125,0.9921875,0.32916668,1,0,1,0,0,0\n",
      "c8dd79406a936789,xclick,/m/03ssj5,1,0.14345992,0.34810126,0.61051005,0.7434312,1,0,0,0,0\n",
      "cbc8443a05cf29cc,xclick,/m/03ssj5,1,0.1171875,0.775,0.68333334,1,0,1,0,0,0\n",
      "cd2ded9ae21c7f0e,xclick,/m/03ssj5,1,0.27286136,0.84070796,0.40486726,1,0,0,0,0,0\n",
      "ce442e81fded4107,xclick,/m/03ssj5,1,0.056047197,0.68289083,0.2920354,1,0,0,0,0,0\n",
      "d02bee6224ca943a,xclick,/m/03ssj5,1,0.19375,0.7953125,0.40938166,0.6076759,0,0,0,0,0\n",
      "d0e26d6efd457fc8,xclick,/m/03ssj5,1,0.4577573,0.9984639,0.33545646,0.9978769,1,1,0,0,0\n",
      "d794d9f52414c141,xclick,/m/03ssj5,1,0,1,0.48541668,1,0,0,0,0,0\n",
      "ddd8b588942ceded,xclick,/m/03ssj5,1,0.080763586,0.54772395,0.45454547,0.9955654,1,1,0,0,0\n",
      "dea2fdb1a77ec6bb,xclick,/m/03ssj5,1,0.22827688,0.7864507,0.40044248,0.9977876,0,0,0,0,0\n",
      "dea2fdb1a77ec6bb,xclick,/m/03ssj5,1,0.4388807,0.9381443,0.40044248,0.9380531,1,0,0,0,0\n",
      "e2089d2390b6031c,xclick,/m/03ssj5,1,0.14126395,0.94919455,0.010526316,1,0,0,0,0,0\n",
      "e3d0e1cc6109fa71,xclick,/m/03ssj5,1,0.012635379,0.49458483,0.71299636,0.9783394,0,1,0,0,0\n",
      "e3d0e1cc6109fa71,xclick,/m/03ssj5,1,0.014440433,0.49097472,0.1534296,0.49458483,0,1,0,0,0\n",
      "e3d0e1cc6109fa71,xclick,/m/03ssj5,1,0.50361013,0.98194945,0.18050541,0.49097472,0,1,0,0,0\n",
      "e3d0e1cc6109fa71,xclick,/m/03ssj5,1,0.5072202,0.9855596,0.69133574,0.98736465,0,1,0,0,0\n",
      "e7fe54073a9a84bc,xclick,/m/03ssj5,1,0,1,0.4532925,1,0,1,0,0,0\n",
      "e83c957123f20a19,xclick,/m/03ssj5,1,0.034285713,0.94857144,0.23428571,1,0,0,0,0,0\n",
      "eb959eb331e1d4ca,xclick,/m/03ssj5,1,0.24941176,0.75529414,0.5017544,1,1,0,0,0,0\n",
      "ee57bbfb0ebab0a7,xclick,/m/03ssj5,1,0.0453125,0.6609375,0.32708332,0.9875,1,1,0,0,0\n",
      "ee57bbfb0ebab0a7,xclick,/m/03ssj5,1,0.48125,1,0.33125,0.99583334,0,1,0,0,0\n",
      "ee57bbfb0ebab0a7,xclick,/m/03ssj5,1,0.70625,1,0.3625,0.99583334,0,1,0,0,0\n",
      "ef87eba394783972,xclick,/m/03ssj5,1,0.0014749262,0.8141593,0.35840708,0.9977876,1,1,0,0,0\n",
      "f2fa3f3c444148f2,xclick,/m/03ssj5,1,0,1,0,1,0,1,0,0,0\n",
      "f312f2af6e9a8073,xclick,/m/03ssj5,1,0,1,0.14073072,1,0,0,0,0,0\n",
      "f6b18f69ec5b4f5c,xclick,/m/03ssj5,1,0.65625,1,0.71458334,1,0,1,0,0,0\n",
      "fa8d6d40dac333ea,xclick,/m/03ssj5,1,0.22895126,0.6410635,0.34437087,0.9977925,0,0,0,0,0\n",
      "fa8d6d40dac333ea,xclick,/m/03ssj5,1,0.65583456,0.7872969,0.22295806,1,0,1,0,0,0\n",
      "\n",
      "Class annotations 2: ['03646bb2f288eaae,xclick,/m/03ssj5,1,0,0.9985229,0.22295806,0.9977925,1,1,0,0,0', '0a6bc386b28f2aac,xclick,/m/03ssj5,1,0.182,0.716,0.536,0.928,0,0,0,0,0', '0a6bc386b28f2aac,xclick,/m/03ssj5,1,0.376,0.99,0.59466666,1,0,0,0,0,0', '0fb3a760f909c8c9,xclick,/m/03ssj5,1,0.28541666,0.99791664,0.48043817,0.9984351,1,1,0,0,0', '1229d5e203956ac6,xclick,/m/03ssj5,1,0,0.3609375,0.18333334,0.775,0,1,0,0,0', '1229d5e203956ac6,xclick,/m/03ssj5,1,0.63125,1,0.26875,0.9791667,0,0,0,0,0', '15e9f230f8f0609f,xclick,/m/03ssj5,1,0,1,0.70625,1,0,1,0,0,0', '17e05469444c8e0a,xclick,/m/03ssj5,1,0,1,0.21666667,1,0,1,0,0,0', '18c8303c966a5ac6,xclick,/m/03ssj5,1,0,1,0.017699115,1,0,1,0,0,0', '18ced18aee04c48e,xclick,/m/03ssj5,1,0,1,0,1,0,1,0,0,0', '1d5bb9d9a8a49e84,xclick,/m/03ssj5,1,0,0.8421875,0.079166666,1,0,0,0,0,0', '208e95b8f33fecda,xclick,/m/03ssj5,1,0,0.6233383,0.5629139,1,0,1,0,0,0', '20af1d21449acdce,xclick,/m/03ssj5,1,0,0.4640625,0,0.48958334,1,1,0,0,0', '2110e5d8ca7e5a99,xclick,/m/03ssj5,1,0,0.9984375,0,0.99791664,1,1,0,0,0', '288150fcf6fcac88,xclick,/m/03ssj5,1,0.396875,1,0.3125,1,1,1,0,0,0', '28a4c2b31f945e5f,xclick,/m/03ssj5,1,0.5097561,0.997561,0.5667276,0.9140768,1,0,0,0,0', '2ac96d4eed4e33c8,xclick,/m/03ssj5,1,0.3578125,0.9984375,0.0020833334,0.66041666,0,1,0,0,0', '2ed000d799794d71,xclick,/m/03ssj5,1,0,1,0.38412017,1,0,1,0,0,0', '36fb4da1df30f769,xclick,/m/03ssj5,1,0,0.9983361,0,0.99607843,1,1,0,0,0', '38f481601bac0d4a,xclick,/m/03ssj5,1,0,0.69264704,0.3436807,1,1,1,0,0,0', '38f481601bac0d4a,xclick,/m/03ssj5,1,0.4779412,0.9044118,0.2949002,0.6895787,1,0,0,0,0', '3a689e83e0e9f6e9,xclick,/m/03ssj5,1,0.39583334,1,0.6125,1,0,0,0,0,0', '3bbc38b3beafe343,xclick,/m/03ssj5,1,0,0.7796875,0.38333333,0.99791664,0,1,0,0,0', '3f65d9a5db068b25,xclick,/m/03ssj5,1,0.0015625,0.859375,0.14583333,1,0,1,0,0,0', '3fb5e100e9962553,xclick,/m/03ssj5,1,0.39290586,1,0.52870816,1,0,0,0,0,0', '408bfc9875660552,xclick,/m/03ssj5,1,0.15408805,0.7279874,0.6141079,1,0,0,0,0,0', '409019c958741906,xclick,/m/03ssj5,1,0.15511551,0.6732673,0.54347825,0.96837944,0,0,0,0,0', '4319a313fae699f9,xclick,/m/03ssj5,1,0,0.41875,0,1,0,1,0,0,0', '4319a313fae699f9,xclick,/m/03ssj5,1,0.3015625,0.5453125,0.054166667,0.97291666,1,0,0,0,0', '4319a313fae699f9,xclick,/m/03ssj5,1,0.4671875,1,0.0375,1,0,1,0,0,0', '44e6ff6555a759f3,xclick,/m/03ssj5,1,0.08915663,0.97590363,0.38700947,0.65764546,0,0,0,0,0', '4775ce4f3625482e,xclick,/m/03ssj5,1,0,0.91041666,0.63055557,1,0,1,0,0,0', '477926ac3bcfb06e,xclick,/m/03ssj5,1,0.225,0.3671875,0.46041667,0.72083336,1,0,0,0,0', '50123869e3d723f1,xclick,/m/03ssj5,1,0,1,0.033333335,1,0,1,0,0,0', '50c91362e9bc580f,xclick,/m/03ssj5,1,0,1,0.016666668,1,0,1,0,0,1', '5991aecc161bb5c8,xclick,/m/03ssj5,1,0,0.9955257,0.24489796,0.8206997,1,1,0,0,0', '5a44d26db9ea0aa8,xclick,/m/03ssj5,1,0,0.99785864,0.12861736,0.9983923,0,1,0,0,0', '5b906e188aac9f24,xclick,/m/03ssj5,1,0,0.77262694,0.42772862,0.53687316,0,1,0,0,0', '5b906e188aac9f24,xclick,/m/03ssj5,1,0,0.777042,0.50884956,0.7699115,0,1,0,0,0', '5e360c07411fb805,xclick,/m/03ssj5,1,0,0.8640625,0.45,0.99791664,1,0,0,0,0', '5ebc104d9d7f872c,xclick,/m/03ssj5,1,0,1,0.004166667,0.99375,0,0,0,0,0', '608fda8c976e0ac5,xclick,/m/03ssj5,1,0.028125,0.778125,0.31458333,1,1,1,0,0,0', '608fda8c976e0ac5,xclick,/m/03ssj5,1,0.3,0.9515625,0.27916667,0.95,1,0,0,0,0', '60fdb7fd333eaf74,xclick,/m/03ssj5,1,0.12195122,0.9376694,0.14457831,0.97590363,0,0,0,0,0', '618dbeb8fb32291f,xclick,/m/03ssj5,1,0,1,0,1,0,1,0,0,0', '63aae2fcaccefba7,xclick,/m/03ssj5,1,0.042424243,1,0.5573506,1,0,1,0,0,0', '65a40656a6ba5344,xclick,/m/03ssj5,1,0,0.9985251,0.044247787,0.9977876,1,1,0,0,0', '666a614b3123dff9,xclick,/m/03ssj5,1,0,0.60625,0.41041666,0.99791664,1,0,0,0,0', '666a614b3123dff9,xclick,/m/03ssj5,1,0.4890625,0.9984375,0.37291667,1,1,0,0,0,0', '68907defc0c961e1,xclick,/m/03ssj5,1,0,0.6125,0.44375,1,0,0,0,0,0', '6c582f286b611dbb,xclick,/m/03ssj5,1,0,1,0.2,1,0,1,0,0,0', '70a82a2501178f8b,xclick,/m/03ssj5,1,0,0.5,0.36041668,0.99791664,0,1,0,0,0', '70a82a2501178f8b,xclick,/m/03ssj5,1,0.5875,0.9890625,0.33541667,0.99791664,1,1,0,0,0', '711f4bda960e3983,xclick,/m/03ssj5,1,0,0.9977876,0.23451327,1,0,0,0,0,0', '74c9a0c1ab44e47f,xclick,/m/03ssj5,1,0,1,0.5469314,1,0,0,0,0,0', '755164cc12b2a690,xclick,/m/03ssj5,1,0,0.9640625,0.34791666,1,0,0,0,0,0', '76c9df7686da333c,xclick,/m/03ssj5,1,0,0.998,0.3233831,0.99751246,0,1,0,0,0', '7c125815652e330a,xclick,/m/03ssj5,1,0,1,0.11283186,1,0,0,0,0,0', '7c3b876233adde13,xclick,/m/03ssj5,1,0,1,0.13053097,1,0,0,0,0,0', '7d97e842bb3c1363,xclick,/m/03ssj5,1,0.01875,0.9890625,0.00625,0.99791664,0,0,0,0,0', '87b1fa47c3ed5a92,xclick,/m/03ssj5,1,0.19411765,0.6602941,0.4767184,1,0,0,0,0,0', '898890757a38a6db,xclick,/m/03ssj5,1,0.27794117,0.68088233,0.37915742,0.8203991,0,0,0,1,0', '89ae6cceba0c6dc7,xclick,/m/03ssj5,1,0,0.8685446,0.40416667,0.99583334,1,1,0,0,0', '8a01d74cbf72e782,xclick,/m/03ssj5,1,0.2109375,0.909375,0.6125,1,0,1,0,0,0', '8afd2f0e67254343,xclick,/m/03ssj5,1,0.5524257,1,0.058333334,0.675,1,0,0,0,0', '8d51d058a5f21775,xclick,/m/03ssj5,1,0.0015625,1,0.027083334,1,0,1,0,0,0', '9295ab265fafd8e5,xclick,/m/03ssj5,1,0,0.96875,0.114583336,0.99791664,1,1,0,0,0', '93641625fc063e6c,xclick,/m/03ssj5,1,0,0.6696165,0,1,0,1,0,0,0', '968d0022f4b1d0dc,xclick,/m/03ssj5,1,0.49103138,0.84753364,0.5334302,1,0,0,0,0,0', '96b573942e14da60,xclick,/m/03ssj5,1,0.36283186,1,0,1,0,1,0,0,0', '96c539fa081cd474,xclick,/m/03ssj5,1,0,0.63274336,0,1,0,1,0,0,0', '96c539fa081cd474,xclick,/m/03ssj5,1,0.71091443,0.9262537,0.17699115,1,1,1,0,0,0', '980fb374c3ec75e3,xclick,/m/03ssj5,1,0.44035345,0.78350514,0.3761062,0.7234513,0,0,0,0,0', '994385c9f33f2075,xclick,/m/03ssj5,1,0.0265625,0.9859375,0.73125,0.99791664,1,1,0,0,0', '994385c9f33f2075,xclick,/m/03ssj5,1,0.0921875,0.359375,0.56041664,0.75,1,0,0,0,0', '9afab31afacbba12,xclick,/m/03ssj5,1,0.79583335,0.94375,0.3828125,0.64375,1,1,0,0,0', '9afab31afacbba12,xclick,/m/03ssj5,1,0.86875,1,0.334375,0.834375,0,1,0,0,0', 'a0db623468bab8d5,xclick,/m/03ssj5,1,0.203125,0.7546875,0.31041667,0.91875,0,0,0,0,0', 'a359ca53d4609faa,xclick,/m/03ssj5,1,0,0.996875,0.49583334,1,0,0,0,0,0', 'a5d3b9952ef78c2e,xclick,/m/03ssj5,1,0.5046875,1,0.41041666,1,0,0,0,0,0', 'a86c99772420c77e,xclick,/m/03ssj5,1,0.30686405,0.66890985,0.43583536,0.8958838,0,0,0,0,0', 'a92113b2150cbc53,xclick,/m/03ssj5,1,0.057902973,0.90766823,0.022916667,1,0,0,0,0,0', 'aa41ff683e2c5a60,xclick,/m/03ssj5,1,0.0020833334,0.64166665,0.275,1,0,1,0,0,0', 'ab9b1e4579fd710b,xclick,/m/03ssj5,1,0.15151516,0.92424244,0.4631579,0.99473685,1,1,0,0,0', 'b5f7b2aa13abe836,xclick,/m/03ssj5,1,0,0.8592058,0.2797834,0.99458486,1,0,0,0,0', 'b613c149d79da0d2,xclick,/m/03ssj5,1,0,0.4234375,0.40416667,1,0,1,0,0,0', 'b613c149d79da0d2,xclick,/m/03ssj5,1,0.2296875,0.8171875,0.35833332,0.98541665,0,0,0,0,0', 'b613c149d79da0d2,xclick,/m/03ssj5,1,0.584375,1,0.33125,0.71875,0,0,0,0,0', 'b6f26c9d7c6667c5,xclick,/m/03ssj5,1,0,1,0.20625,1,0,0,0,0,0', 'b833ff7e8c121fd4,xclick,/m/03ssj5,1,0.3109375,0.95625,0.37916666,1,0,0,0,0,0', 'bce01b280e7be3c9,xclick,/m/03ssj5,1,0,0.80625,0.54791665,1,1,1,0,0,0', 'c08da8484c808dbe,xclick,/m/03ssj5,1,0.6386431,0.7787611,0.5685841,0.63274336,0,0,0,0,0', 'c2b9ea7fa6acb336,xclick,/m/03ssj5,1,0.07782672,0.4743025,0.6607539,1,0,0,0,0,0', 'c2b9ea7fa6acb336,xclick,/m/03ssj5,1,0.40381792,0.5315712,0.44124168,0.6696231,1,0,0,0,0', 'c2ed6ddf764b96d4,xclick,/m/03ssj5,1,0.121875,1,0.65,1,0,1,0,0,0', 'c3287d9a3da51af3,xclick,/m/03ssj5,1,0.18611522,1,0.50110376,1,0,1,0,0,0', 'c4f2aabceda78470,xclick,/m/03ssj5,1,0,1,0.1768953,1,0,0,0,0,0', 'c508797245ca5106,xclick,/m/03ssj5,1,0.18125,0.9921875,0.32916668,1,0,1,0,0,0', 'c8dd79406a936789,xclick,/m/03ssj5,1,0.14345992,0.34810126,0.61051005,0.7434312,1,0,0,0,0', 'cbc8443a05cf29cc,xclick,/m/03ssj5,1,0.1171875,0.775,0.68333334,1,0,1,0,0,0', 'cd2ded9ae21c7f0e,xclick,/m/03ssj5,1,0.27286136,0.84070796,0.40486726,1,0,0,0,0,0', 'ce442e81fded4107,xclick,/m/03ssj5,1,0.056047197,0.68289083,0.2920354,1,0,0,0,0,0', 'd02bee6224ca943a,xclick,/m/03ssj5,1,0.19375,0.7953125,0.40938166,0.6076759,0,0,0,0,0', 'd0e26d6efd457fc8,xclick,/m/03ssj5,1,0.4577573,0.9984639,0.33545646,0.9978769,1,1,0,0,0', 'd794d9f52414c141,xclick,/m/03ssj5,1,0,1,0.48541668,1,0,0,0,0,0', 'ddd8b588942ceded,xclick,/m/03ssj5,1,0.080763586,0.54772395,0.45454547,0.9955654,1,1,0,0,0', 'dea2fdb1a77ec6bb,xclick,/m/03ssj5,1,0.22827688,0.7864507,0.40044248,0.9977876,0,0,0,0,0', 'dea2fdb1a77ec6bb,xclick,/m/03ssj5,1,0.4388807,0.9381443,0.40044248,0.9380531,1,0,0,0,0', 'e2089d2390b6031c,xclick,/m/03ssj5,1,0.14126395,0.94919455,0.010526316,1,0,0,0,0,0', 'e3d0e1cc6109fa71,xclick,/m/03ssj5,1,0.012635379,0.49458483,0.71299636,0.9783394,0,1,0,0,0', 'e3d0e1cc6109fa71,xclick,/m/03ssj5,1,0.014440433,0.49097472,0.1534296,0.49458483,0,1,0,0,0', 'e3d0e1cc6109fa71,xclick,/m/03ssj5,1,0.50361013,0.98194945,0.18050541,0.49097472,0,1,0,0,0', 'e3d0e1cc6109fa71,xclick,/m/03ssj5,1,0.5072202,0.9855596,0.69133574,0.98736465,0,1,0,0,0', 'e7fe54073a9a84bc,xclick,/m/03ssj5,1,0,1,0.4532925,1,0,1,0,0,0', 'e83c957123f20a19,xclick,/m/03ssj5,1,0.034285713,0.94857144,0.23428571,1,0,0,0,0,0', 'eb959eb331e1d4ca,xclick,/m/03ssj5,1,0.24941176,0.75529414,0.5017544,1,1,0,0,0,0', 'ee57bbfb0ebab0a7,xclick,/m/03ssj5,1,0.0453125,0.6609375,0.32708332,0.9875,1,1,0,0,0', 'ee57bbfb0ebab0a7,xclick,/m/03ssj5,1,0.48125,1,0.33125,0.99583334,0,1,0,0,0', 'ee57bbfb0ebab0a7,xclick,/m/03ssj5,1,0.70625,1,0.3625,0.99583334,0,1,0,0,0', 'ef87eba394783972,xclick,/m/03ssj5,1,0.0014749262,0.8141593,0.35840708,0.9977876,1,1,0,0,0', 'f2fa3f3c444148f2,xclick,/m/03ssj5,1,0,1,0,1,0,1,0,0,0', 'f312f2af6e9a8073,xclick,/m/03ssj5,1,0,1,0.14073072,1,0,0,0,0,0', 'f6b18f69ec5b4f5c,xclick,/m/03ssj5,1,0.65625,1,0.71458334,1,0,1,0,0,0', 'fa8d6d40dac333ea,xclick,/m/03ssj5,1,0.22895126,0.6410635,0.34437087,0.9977925,0,0,0,0,0', 'fa8d6d40dac333ea,xclick,/m/03ssj5,1,0.65583456,0.7872969,0.22295806,1,0,1,0,0,0']\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/03646bb2f288eaae.jpg validation/03646bb2f288eaae.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/0a6bc386b28f2aac.jpg validation/0a6bc386b28f2aac.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/0a6bc386b28f2aac.jpg validation/0a6bc386b28f2aac.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/0fb3a760f909c8c9.jpg validation/0fb3a760f909c8c9.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/1229d5e203956ac6.jpg validation/1229d5e203956ac6.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/1229d5e203956ac6.jpg validation/1229d5e203956ac6.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/15e9f230f8f0609f.jpg validation/15e9f230f8f0609f.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/17e05469444c8e0a.jpg validation/17e05469444c8e0a.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/18c8303c966a5ac6.jpg validation/18c8303c966a5ac6.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/18ced18aee04c48e.jpg validation/18ced18aee04c48e.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/1d5bb9d9a8a49e84.jpg validation/1d5bb9d9a8a49e84.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/208e95b8f33fecda.jpg validation/208e95b8f33fecda.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/20af1d21449acdce.jpg validation/20af1d21449acdce.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/2110e5d8ca7e5a99.jpg validation/2110e5d8ca7e5a99.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/288150fcf6fcac88.jpg validation/288150fcf6fcac88.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/28a4c2b31f945e5f.jpg validation/28a4c2b31f945e5f.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/2ac96d4eed4e33c8.jpg validation/2ac96d4eed4e33c8.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/2ed000d799794d71.jpg validation/2ed000d799794d71.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/36fb4da1df30f769.jpg validation/36fb4da1df30f769.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/38f481601bac0d4a.jpg validation/38f481601bac0d4a.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/38f481601bac0d4a.jpg validation/38f481601bac0d4a.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/3a689e83e0e9f6e9.jpg validation/3a689e83e0e9f6e9.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/3bbc38b3beafe343.jpg validation/3bbc38b3beafe343.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/3f65d9a5db068b25.jpg validation/3f65d9a5db068b25.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/3fb5e100e9962553.jpg validation/3fb5e100e9962553.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/408bfc9875660552.jpg validation/408bfc9875660552.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/409019c958741906.jpg validation/409019c958741906.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/4319a313fae699f9.jpg validation/4319a313fae699f9.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/4319a313fae699f9.jpg validation/4319a313fae699f9.jpg\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/4319a313fae699f9.jpg validation/4319a313fae699f9.jpg\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/44e6ff6555a759f3.jpg validation/44e6ff6555a759f3.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/4775ce4f3625482e.jpg validation/4775ce4f3625482e.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/477926ac3bcfb06e.jpg validation/477926ac3bcfb06e.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/50123869e3d723f1.jpg validation/50123869e3d723f1.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/50c91362e9bc580f.jpg validation/50c91362e9bc580f.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/5991aecc161bb5c8.jpg validation/5991aecc161bb5c8.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/5a44d26db9ea0aa8.jpg validation/5a44d26db9ea0aa8.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/5b906e188aac9f24.jpg validation/5b906e188aac9f24.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/5b906e188aac9f24.jpg validation/5b906e188aac9f24.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/5e360c07411fb805.jpg validation/5e360c07411fb805.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/5ebc104d9d7f872c.jpg validation/5ebc104d9d7f872c.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/608fda8c976e0ac5.jpg validation/608fda8c976e0ac5.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/608fda8c976e0ac5.jpg validation/608fda8c976e0ac5.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/60fdb7fd333eaf74.jpg validation/60fdb7fd333eaf74.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/618dbeb8fb32291f.jpg validation/618dbeb8fb32291f.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/63aae2fcaccefba7.jpg validation/63aae2fcaccefba7.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/65a40656a6ba5344.jpg validation/65a40656a6ba5344.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/666a614b3123dff9.jpg validation/666a614b3123dff9.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/666a614b3123dff9.jpg validation/666a614b3123dff9.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/68907defc0c961e1.jpg validation/68907defc0c961e1.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/6c582f286b611dbb.jpg validation/6c582f286b611dbb.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/70a82a2501178f8b.jpg validation/70a82a2501178f8b.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/70a82a2501178f8b.jpg validation/70a82a2501178f8b.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/711f4bda960e3983.jpg validation/711f4bda960e3983.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/74c9a0c1ab44e47f.jpg validation/74c9a0c1ab44e47f.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/755164cc12b2a690.jpg validation/755164cc12b2a690.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/76c9df7686da333c.jpg validation/76c9df7686da333c.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/7c125815652e330a.jpg validation/7c125815652e330a.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/7c3b876233adde13.jpg validation/7c3b876233adde13.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/7d97e842bb3c1363.jpg validation/7d97e842bb3c1363.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/87b1fa47c3ed5a92.jpg validation/87b1fa47c3ed5a92.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/898890757a38a6db.jpg validation/898890757a38a6db.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/89ae6cceba0c6dc7.jpg validation/89ae6cceba0c6dc7.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/8a01d74cbf72e782.jpg validation/8a01d74cbf72e782.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/8afd2f0e67254343.jpg validation/8afd2f0e67254343.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/8d51d058a5f21775.jpg validation/8d51d058a5f21775.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/9295ab265fafd8e5.jpg validation/9295ab265fafd8e5.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/93641625fc063e6c.jpg validation/93641625fc063e6c.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/968d0022f4b1d0dc.jpg validation/968d0022f4b1d0dc.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/96b573942e14da60.jpg validation/96b573942e14da60.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/96c539fa081cd474.jpg validation/96c539fa081cd474.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/96c539fa081cd474.jpg validation/96c539fa081cd474.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/980fb374c3ec75e3.jpg validation/980fb374c3ec75e3.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/994385c9f33f2075.jpg validation/994385c9f33f2075.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/994385c9f33f2075.jpg validation/994385c9f33f2075.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/9afab31afacbba12.jpg validation/9afab31afacbba12.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/9afab31afacbba12.jpg validation/9afab31afacbba12.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/a0db623468bab8d5.jpg validation/a0db623468bab8d5.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/a359ca53d4609faa.jpg validation/a359ca53d4609faa.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/a5d3b9952ef78c2e.jpg validation/a5d3b9952ef78c2e.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/a86c99772420c77e.jpg validation/a86c99772420c77e.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/a92113b2150cbc53.jpg validation/a92113b2150cbc53.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/aa41ff683e2c5a60.jpg validation/aa41ff683e2c5a60.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/ab9b1e4579fd710b.jpg validation/ab9b1e4579fd710b.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/b5f7b2aa13abe836.jpg validation/b5f7b2aa13abe836.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/b613c149d79da0d2.jpg validation/b613c149d79da0d2.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/b613c149d79da0d2.jpg validation/b613c149d79da0d2.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/b613c149d79da0d2.jpg validation/b613c149d79da0d2.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/b6f26c9d7c6667c5.jpg validation/b6f26c9d7c6667c5.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/b833ff7e8c121fd4.jpg validation/b833ff7e8c121fd4.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/bce01b280e7be3c9.jpg validation/bce01b280e7be3c9.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/c08da8484c808dbe.jpg validation/c08da8484c808dbe.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/c2b9ea7fa6acb336.jpg validation/c2b9ea7fa6acb336.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/c2b9ea7fa6acb336.jpg validation/c2b9ea7fa6acb336.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/c2ed6ddf764b96d4.jpg validation/c2ed6ddf764b96d4.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/c3287d9a3da51af3.jpg validation/c3287d9a3da51af3.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/c4f2aabceda78470.jpg validation/c4f2aabceda78470.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/c508797245ca5106.jpg validation/c508797245ca5106.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/c8dd79406a936789.jpg validation/c8dd79406a936789.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/cbc8443a05cf29cc.jpg validation/cbc8443a05cf29cc.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/cd2ded9ae21c7f0e.jpg validation/cd2ded9ae21c7f0e.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/ce442e81fded4107.jpg validation/ce442e81fded4107.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/d02bee6224ca943a.jpg validation/d02bee6224ca943a.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/d0e26d6efd457fc8.jpg validation/d0e26d6efd457fc8.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/d794d9f52414c141.jpg validation/d794d9f52414c141.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/ddd8b588942ceded.jpg validation/ddd8b588942ceded.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/dea2fdb1a77ec6bb.jpg validation/dea2fdb1a77ec6bb.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/dea2fdb1a77ec6bb.jpg validation/dea2fdb1a77ec6bb.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/e2089d2390b6031c.jpg validation/e2089d2390b6031c.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/e3d0e1cc6109fa71.jpg validation/e3d0e1cc6109fa71.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/e3d0e1cc6109fa71.jpg validation/e3d0e1cc6109fa71.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/e3d0e1cc6109fa71.jpg validation/e3d0e1cc6109fa71.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/e3d0e1cc6109fa71.jpg validation/e3d0e1cc6109fa71.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/e7fe54073a9a84bc.jpg validation/e7fe54073a9a84bc.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/e83c957123f20a19.jpg validation/e83c957123f20a19.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/eb959eb331e1d4ca.jpg validation/eb959eb331e1d4ca.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/ee57bbfb0ebab0a7.jpg validation/ee57bbfb0ebab0a7.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/ee57bbfb0ebab0a7.jpg validation/ee57bbfb0ebab0a7.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/ee57bbfb0ebab0a7.jpg validation/ee57bbfb0ebab0a7.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/ef87eba394783972.jpg validation/ef87eba394783972.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/f2fa3f3c444148f2.jpg validation/f2fa3f3c444148f2.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/f312f2af6e9a8073.jpg validation/f312f2af6e9a8073.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/f6b18f69ec5b4f5c.jpg validation/f6b18f69ec5b4f5c.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/fa8d6d40dac333ea.jpg validation/fa8d6d40dac333ea.jpg\r\n",
      "Command 2: aws s3 --no-sign-request --only-show-errors cp s3://open-images-dataset/validation/fa8d6d40dac333ea.jpg validation/fa8d6d40dac333ea.jpg\r\n",
      "Downloading: 103 images | Num classes: 1 | Dataset: validation\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 103/103 [01:38<00:00,  1.05it/s]\n"
     ]
    }
   ],
   "source": [
    "!python3 downloadOI.py --classes 'Bed' --dataset validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Toilet,Swimming_pool,Bed,Billiard_table,Sink,Fountain,Oven,Ceiling_fan,Television,Microwave_oven,Gas_stove,Refrigerator,Kitchen_&_dining_room_table,Washing_machine,Bathtub,Stairs,Fireplace,Pillow,Mirror,Shower,Couch,Countertop,Coffeemaker,Dishwasher,Sofa_bed,Tree_house,Towel,Porch,Wine_rack,Jacuzzi,'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn subset of target classes into a long string for the command line\n",
    "subset_string = str()\n",
    "for i in subset:\n",
    "    subset_string += str(i) + \",\"\n",
    "subset_string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try download all the target images\n",
    "\n",
    "I'm going to now try and download all the target images (subset of classes) from the Open Images validation set.\n",
    "\n",
    "It would be good to have a function which downloaded strictly images of a certain class based on a list.\n",
    "\n",
    "Then created the labels later... that would save me from manipulating all the images I download with `downloadOI.py`..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !python3 downloadOI.py --classes 'Toilet,Swimming_pool,Bed,Billiard_table,Sink,Fountain,Oven,Ceiling_fan,Television,Microwave_oven,Gas_stove,Refrigerator,Kitchen_&_dining_room_table,Washing_machine,Bathtub,Stairs,Fireplace,Pillow,Mirror,Shower,Couch,Countertop,Coffeemaker,Dishwasher,Sofa_bed,Tree_house,Towel,Porch,Wine_rack,Jacuzzi' --mode validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting images/labels to Detectron2 style\n",
    "Now I've got images ready, my goal is to now convert their current style them into Detectron2 style (see below).\n",
    "\n",
    "I'll need a way to:\n",
    "1. Traverse different file paths\n",
    "2. Gather filenames and explore their text contents\n",
    "3. Seek duplicates and make sure they contain multiple labels\n",
    "\n",
    "What I should end up with is:\n",
    "- A single file of images (can convert this to train/test later)\n",
    "- A dictionary of all of the different parameters for each image path\n",
    "    - Perhaps I could do this with a Pandas dataframe? So it's visual? Then convert it to JSON maybe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "detectron2_style = {\n",
    "   'annotations': [{'bbox': [994, 619, 1445, 1166],\n",
    "   'bbox_mode': \"BoxMode.XYXY_ABS\", ## NOTE: this isn't a string in the actual label\n",
    "   'category_id': 0,\n",
    "   'iscrowd': 0,\n",
    "   'segmentation': [[1020.5,\n",
    "     963.5,\n",
    "     1000.5,\n",
    "     841.5,\n",
    "     #### Truncated ####\n",
    "     989.5,\n",
    "     1020.5,\n",
    "     963.5]]}],\n",
    " 'file_name': 'balloon/train/34020010494_e5cb88e1c4_k.jpg',\n",
    " 'height': 1536,\n",
    " 'image_id': 0,\n",
    " 'width': 2048\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Image ID's\n",
    "\n",
    "# TODO - check to see if all image ID's are unique (remove duplicates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image_ids(image_folder=None):\n",
    "    \"\"\"\n",
    "    Explores a folder of images and gets their ID from their file name.\n",
    "    Returns a list of all image ID's in image_folder.\n",
    "    E.g. image_folder/608fda8c976e0ac.jpg -> [\"608fda8c976e0ac\"]\n",
    "    \n",
    "    Params\n",
    "    ------\n",
    "    image_folder (str): path to folder of images, e.g. \"../validation/\"\n",
    "    \"\"\"\n",
    "    return [os.path.splitext(img_name)[0] for img_name in os.listdir(image_folder)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['d0e26d6efd457fc8',\n",
       " '608fda8c976e0ac5',\n",
       " '898890757a38a6db',\n",
       " 'ab9b1e4579fd710b',\n",
       " '3fb5e100e9962553',\n",
       " '5e360c07411fb805',\n",
       " '50c91362e9bc580f',\n",
       " '408bfc9875660552',\n",
       " 'eb959eb331e1d4ca',\n",
       " '8a01d74cbf72e782']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_image_ids = get_image_ids(\"validation\")\n",
    "val_image_ids[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(val_image_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulate annotations based on the images we're using\n",
    "\n",
    "I should write a function which:\n",
    "- Gets my image IDs\n",
    "- Matches them with their details in the annotations csv \n",
    "- Updates the annotations csv based on the unique images we're using"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a function which formats a specific annotations csv based on what we're dealing with\n",
    "def format_annotations(image_folder, annotation_file):\n",
    "    \"\"\"\n",
    "    TODO - NOTE: This function could (definitely can) be faster.\n",
    "    TODO - Some ideas: skip the use of pandas entirely and use CSV's\n",
    "    \n",
    "    Formats annotation_file based on images contained in image_folder.\n",
    "    Will get all unique image IDs and make sure annotation_file\n",
    "    only contains those (the target images).\n",
    "    Adds meta-data to annotation_file such as class names and categories.\n",
    "    Note: image_folder and annotation_file should both be validation if working on\n",
    "    validation set or both be training if working on training set.\n",
    "    \n",
    "    Params\n",
    "    ------\n",
    "    image_folder (str): path to folder of target images.\n",
    "    annotation_file (str): path to annotation file of target images.\n",
    "    \"\"\"\n",
    "    # Get all image ids from target directory\n",
    "    image_ids = get_image_ids(image_folder)\n",
    "    \n",
    "    # Setup annotation file and classnames\n",
    "    annot_file = pd.read_csv(annotation_file)\n",
    "    classes = pd.read_csv(\"class-descriptions-boxable.csv\",\n",
    "                          names=[\"LabelName\", \"ClassName\"])\n",
    "    \n",
    "    # Create classname column on annotations which converts label codes to string labels\n",
    "    annot_file[\"ClassName\"] = annot_file[\"LabelName\"].map(classes.set_index(\"LabelName\")[\"ClassName\"])\n",
    "    \n",
    "    # Make sure we only get the images we're concerned about\n",
    "    annot_file = annot_file[annot_file[\"ImageID\"].isin(image_ids)]\n",
    "   \n",
    "    # Add ClassID column, e.g. \"Bathtub, Toilet\" -> 1, 2\n",
    "    annot_file[\"ClassID\"] = pd.Categorical(annot_file[\"ClassName\"]).codes\n",
    "    \n",
    "    return annot_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 580 ms, sys: 139 ms, total: 718 ms\n",
      "Wall time: 843 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ImageID</th>\n",
       "      <th>Source</th>\n",
       "      <th>LabelName</th>\n",
       "      <th>Confidence</th>\n",
       "      <th>XMin</th>\n",
       "      <th>XMax</th>\n",
       "      <th>YMin</th>\n",
       "      <th>YMax</th>\n",
       "      <th>IsOccluded</th>\n",
       "      <th>IsTruncated</th>\n",
       "      <th>IsGroupOf</th>\n",
       "      <th>IsDepiction</th>\n",
       "      <th>IsInside</th>\n",
       "      <th>ClassName</th>\n",
       "      <th>ClassID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4220</th>\n",
       "      <td>03646bb2f288eaae</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0bt9lr</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.595273</td>\n",
       "      <td>0.198676</td>\n",
       "      <td>0.662252</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Dog</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4221</th>\n",
       "      <td>03646bb2f288eaae</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/09j2d</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.723781</td>\n",
       "      <td>0.233996</td>\n",
       "      <td>0.635762</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Clothing</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4222</th>\n",
       "      <td>03646bb2f288eaae</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/03ssj5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.998523</td>\n",
       "      <td>0.222958</td>\n",
       "      <td>0.997792</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Bed</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12322</th>\n",
       "      <td>0a6bc386b28f2aac</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/01mzpv</td>\n",
       "      <td>1</td>\n",
       "      <td>0.282</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.405333</td>\n",
       "      <td>0.632000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Chair</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12323</th>\n",
       "      <td>0a6bc386b28f2aac</td>\n",
       "      <td>xclick</td>\n",
       "      <td>/m/0c_jw</td>\n",
       "      <td>1</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.998000</td>\n",
       "      <td>0.498667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ImageID  Source  LabelName  Confidence   XMin      XMax  \\\n",
       "4220   03646bb2f288eaae  xclick  /m/0bt9lr           1  0.000  0.595273   \n",
       "4221   03646bb2f288eaae  xclick   /m/09j2d           1  0.000  0.723781   \n",
       "4222   03646bb2f288eaae  xclick  /m/03ssj5           1  0.000  0.998523   \n",
       "12322  0a6bc386b28f2aac  xclick  /m/01mzpv           1  0.282  0.410000   \n",
       "12323  0a6bc386b28f2aac  xclick   /m/0c_jw           1  0.184  0.998000   \n",
       "\n",
       "           YMin      YMax  IsOccluded  IsTruncated  IsGroupOf  IsDepiction  \\\n",
       "4220   0.198676  0.662252           0            1          0            0   \n",
       "4221   0.233996  0.635762           0            1          0            0   \n",
       "4222   0.222958  0.997792           1            1          0            0   \n",
       "12322  0.405333  0.632000           1            0          0            0   \n",
       "12323  0.498667  1.000000           0            0          0            0   \n",
       "\n",
       "       IsInside  ClassName  ClassID  \n",
       "4220          0        Dog       17  \n",
       "4221          0   Clothing       10  \n",
       "4222          0        Bed        1  \n",
       "12322         0      Chair        7  \n",
       "12323         0  Furniture       24  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "val_annot = format_annotations(\"validation\", \"validation-annotations-bbox.csv\")\n",
    "val_annot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start creating dataset dictionaries (how Detectron2 takes inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import CV2 for getting height & width of image\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next: functionise the below code\n",
    "def get_image_dicts(image_folder, annotations):\n",
    "    \"\"\"\n",
    "    Returns Detectron2 style labels of images in image_folder based on data in annotations.\n",
    "    \n",
    "    TODO -- Maybe create some verbosity here? AKA, what are the outputs?\n",
    "    TODO -- what if annotations = None? Can we create a call to create an annotations CSV in 1 hit?\n",
    "    \n",
    "    Params\n",
    "    ------\n",
    "    image_folder (str): target folder containing images\n",
    "    annotations (DataFrame): DataFrame of image label data\n",
    "    \"\"\"\n",
    "    # Get all unique image ids from target folder\n",
    "    img_ids = get_image_ids(image_folder)\n",
    "    \n",
    "    # Start creating image dictionaries (Detectron2 style labelling)\n",
    "    img_dicts = []\n",
    "    for img in img_ids:\n",
    "        record = {}\n",
    "        \n",
    "        # Get image metadata\n",
    "        file_name = image_folder + \"/\" + img + \".jpg\"\n",
    "        height, width = cv2.imread(file_name).shape[:2]\n",
    "        img_data = annotations[annotations[\"ImageID\"] == img].reset_index() # reset index important for images\n",
    "                                                                            # with multiple objects\n",
    "        # Update record dictionary\n",
    "        record[\"file_name\"] = file_name\n",
    "        record[\"image_id\"] = img\n",
    "        record[\"height\"] = height\n",
    "        record[\"width\"] = width\n",
    "        \n",
    "        # Create list of image annotations (labels)\n",
    "        img_annotations = []\n",
    "        for i in range(len(img_data)): # this is where we loop through examples with multiple objects in an image\n",
    "            category_id = img_data.loc[i][\"ClassID\"]\n",
    "            # Get bounding box coordinates in Detectron2 style (x0, y0, x1, y1)\n",
    "            bbox = [img_data.loc[i][[\"XMin\", \"YMin\", \"XMax\", \"YMax\"]].values]\n",
    "            # Setup annot (1 annot = 1 label, there might be more) dictionary\n",
    "            annot = {\n",
    "                \"bbox\": bbox,\n",
    "                \"bbox_mode\": 0, # TODO - Will be: https://detectron2.readthedocs.io/modules/structures.html#detectron2.structures.BoxMode.XYXY_REL\n",
    "                \"category_id\": category_id\n",
    "            }\n",
    "            img_annotations.append(annot)\n",
    "            \n",
    "        # Update record dictionary with annotations\n",
    "        record[\"annotations\"] = img_annotations\n",
    "        \n",
    "        # Add record dictionary with image annotations to img_dicts list\n",
    "        img_dicts.append(record)\n",
    "    return img_dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.74 s, sys: 153 ms, total: 3.9 s\n",
      "Wall time: 5.09 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "val_img_dicts = get_image_dicts(\"validation\", val_annot)\n",
    "len(val_img_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'file_name': 'validation/18c8303c966a5ac6.jpg',\n",
       " 'image_id': '18c8303c966a5ac6',\n",
       " 'height': 1024,\n",
       " 'width': 682,\n",
       " 'annotations': [{'bbox': [array([0.0, 0.064896755, 0.5243363, 0.37020648], dtype=object)],\n",
       "   'bbox_mode': 0,\n",
       "   'category_id': 51},\n",
       "  {'bbox': [array([0.23451327, 0.036873157999999996, 0.75884956, 0.33038348],\n",
       "          dtype=object)],\n",
       "   'bbox_mode': 0,\n",
       "   'category_id': 51},\n",
       "  {'bbox': [array([0.47787609999999997, 0.053097345, 1.0, 0.35103244], dtype=object)],\n",
       "   'bbox_mode': 0,\n",
       "   'category_id': 51},\n",
       "  {'bbox': [array([0.0, 0.017699115, 1.0, 1.0], dtype=object)],\n",
       "   'bbox_mode': 0,\n",
       "   'category_id': 1}]}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check labels for random image\n",
    "# TODO - make this visual\n",
    "import random\n",
    "number = round(random.randint(0, len(val_img_dicts)))\n",
    "print(number)\n",
    "val_img_dicts[number]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detectron2 example style:\n",
    "```\n",
    "{'annotations': [{'bbox': [994, 619, 1445, 1166],\n",
    "   'bbox_mode': <BoxMode.XYXY_ABS: 0>,\n",
    "   'category_id': 0,\n",
    "   'iscrowd': 0,\n",
    "   'segmentation': [[1020.5,\n",
    "     963.5,\n",
    "     1000.5,\n",
    "     841.5,\n",
    "\t\t ... #### Truncated ####\n",
    "     989.5,\n",
    "     1020.5,\n",
    "     963.5]]}],\n",
    " 'file_name': 'balloon/train/34020010494_e5cb88e1c4_k.jpg',\n",
    " 'height': 1536,\n",
    " 'image_id': 0,\n",
    " 'width': 2048}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TODO - View an annotated image\n",
    "\n",
    "Now I've got a way to generate labels, I should be visualizing them as much as I can to make sure they're correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"images/5ba65f801a71d6b7.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TK: Could I turn this into a script? Instead of it being in a notebook?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'_csv.reader' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-71c69f9da2a3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mreader\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: '_csv.reader' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "with open(\"validation-annotations-bbox.csv\") as file:\n",
    "    reader = csv.reader(file)   \n",
    "reader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Tortoise': '/m/011k07',\n",
       " 'Container': '/m/011q46kg',\n",
       " 'Magpie': '/m/012074',\n",
       " 'Sea turtle': '/m/0120dh',\n",
       " 'Football': '/m/01226z',\n",
       " 'Ambulance': '/m/012n7d',\n",
       " 'Ladder': '/m/012w5l',\n",
       " 'Toothbrush': '/m/012xff',\n",
       " 'Syringe': '/m/012ysf',\n",
       " 'Sink': '/m/0130jx',\n",
       " 'Toy': '/m/0138tl',\n",
       " 'Organ': '/m/013y1f',\n",
       " 'Cassette deck': '/m/01432t',\n",
       " 'Apple': '/m/014j1m',\n",
       " 'Human eye': '/m/014sv8',\n",
       " 'Cosmetics': '/m/014trl',\n",
       " 'Paddle': '/m/014y4n',\n",
       " 'Snowman': '/m/0152hh',\n",
       " 'Beer': '/m/01599',\n",
       " 'Chopsticks': '/m/01_5g',\n",
       " 'Human beard': '/m/015h_t',\n",
       " 'Bird': '/m/015p6',\n",
       " 'Parking meter': '/m/015qbp',\n",
       " 'Traffic light': '/m/015qff',\n",
       " 'Croissant': '/m/015wgc',\n",
       " 'Cucumber': '/m/015x4r',\n",
       " 'Radish': '/m/015x5n',\n",
       " 'Towel': '/m/0162_1',\n",
       " 'Doll': '/m/0167gd',\n",
       " 'Skull': '/m/016m2d',\n",
       " 'Washing machine': '/m/0174k2',\n",
       " 'Glove': '/m/0174n1',\n",
       " 'Tick': '/m/0175cv',\n",
       " 'Belt': '/m/0176mf',\n",
       " 'Sunglasses': '/m/017ftj',\n",
       " 'Banjo': '/m/018j2',\n",
       " 'Cart': '/m/018p4k',\n",
       " 'Ball': '/m/018xm',\n",
       " 'Backpack': '/m/01940j',\n",
       " 'Bicycle': '/m/0199g',\n",
       " 'Home appliance': '/m/019dx1',\n",
       " 'Centipede': '/m/019h78',\n",
       " 'Boat': '/m/019jd',\n",
       " 'Surfboard': '/m/019w40',\n",
       " 'Boot': '/m/01b638',\n",
       " 'Headphones': '/m/01b7fy',\n",
       " 'Hot dog': '/m/01b9xk',\n",
       " 'Shorts': '/m/01bfm9',\n",
       " 'Fast food': '/m/01_bhs',\n",
       " 'Bus': '/m/01bjv',\n",
       " 'Boy': '/m/01bl7v',\n",
       " 'Screwdriver': '/m/01bms0',\n",
       " 'Bicycle wheel': '/m/01bqk0',\n",
       " 'Barge': '/m/01btn',\n",
       " 'Laptop': '/m/01c648',\n",
       " 'Miniskirt': '/m/01cmb2',\n",
       " 'Drill': '/m/01d380',\n",
       " 'Dress': '/m/01d40f',\n",
       " 'Bear': '/m/01dws',\n",
       " 'Waffle': '/m/01dwsz',\n",
       " 'Pancake': '/m/01dwwc',\n",
       " 'Brown bear': '/m/01dxs',\n",
       " 'Woodpecker': '/m/01dy8n',\n",
       " 'Blue jay': '/m/01f8m5',\n",
       " 'Pretzel': '/m/01f91_',\n",
       " 'Bagel': '/m/01fb_0',\n",
       " 'Tower': '/m/01fdzj',\n",
       " 'Teapot': '/m/01fh4r',\n",
       " 'Person': '/m/01g317',\n",
       " 'Bow and arrow': '/m/01g3x7',\n",
       " 'Swimwear': '/m/01gkx_',\n",
       " 'Beehive': '/m/01gllr',\n",
       " 'Brassiere': '/m/01gmv2',\n",
       " 'Bee': '/m/01h3n',\n",
       " 'Bat': '/m/01h44',\n",
       " 'Starfish': '/m/01h8tj',\n",
       " 'Popcorn': '/m/01hrv5',\n",
       " 'Burrito': '/m/01j3zr',\n",
       " 'Chainsaw': '/m/01j4z9',\n",
       " 'Balloon': '/m/01j51',\n",
       " 'Wrench': '/m/01j5ks',\n",
       " 'Tent': '/m/01j61q',\n",
       " 'Vehicle registration plate': '/m/01jfm_',\n",
       " 'Lantern': '/m/01jfsr',\n",
       " 'Toaster': '/m/01k6s3',\n",
       " 'Flashlight': '/m/01kb5b',\n",
       " 'Billboard': '/m/01knjb',\n",
       " 'Tiara': '/m/01krhy',\n",
       " 'Limousine': '/m/01lcw4',\n",
       " 'Necklace': '/m/01llwg',\n",
       " 'Carnivore': '/m/01lrl',\n",
       " 'Scissors': '/m/01lsmm',\n",
       " 'Stairs': '/m/01lynh',\n",
       " 'Computer keyboard': '/m/01m2v',\n",
       " 'Printer': '/m/01m4t',\n",
       " 'Traffic sign': '/m/01mqdt',\n",
       " 'Chair': '/m/01mzpv',\n",
       " 'Shirt': '/m/01n4qj',\n",
       " 'Poster': '/m/01n5jq',\n",
       " 'Cheese': '/m/01nkt',\n",
       " 'Sock': '/m/01nq26',\n",
       " 'Fire hydrant': '/m/01pns0',\n",
       " 'Land vehicle': '/m/01prls',\n",
       " 'Earrings': '/m/01r546',\n",
       " 'Tie': '/m/01rkbr',\n",
       " 'Watercraft': '/m/01rzcn',\n",
       " 'Cabinetry': '/m/01s105',\n",
       " 'Suitcase': '/m/01s55n',\n",
       " 'Muffin': '/m/01tcjp',\n",
       " 'Bidet': '/m/01vbnl',\n",
       " 'Snack': '/m/01ww8y',\n",
       " 'Snowmobile': '/m/01x3jk',\n",
       " 'Clock': '/m/01x3z',\n",
       " 'Medical equipment': '/m/01xgg_',\n",
       " 'Cattle': '/m/01xq0k1',\n",
       " 'Cello': '/m/01xqw',\n",
       " 'Jet ski': '/m/01xs3r',\n",
       " 'Camel': '/m/01x_v',\n",
       " 'Coat': '/m/01xygc',\n",
       " 'Suit': '/m/01xyhv',\n",
       " 'Desk': '/m/01y9k5',\n",
       " 'Cat': '/m/01yrx',\n",
       " 'Bronze sculpture': '/m/01yx86',\n",
       " 'Juice': '/m/01z1kdw',\n",
       " 'Gondola': '/m/02068x',\n",
       " 'Beetle': '/m/020jm',\n",
       " 'Cannon': '/m/020kz',\n",
       " 'Computer mouse': '/m/020lf',\n",
       " 'Cookie': '/m/021mn',\n",
       " 'Office building': '/m/021sj1',\n",
       " 'Fountain': '/m/0220r2',\n",
       " 'Coin': '/m/0242l',\n",
       " 'Calculator': '/m/024d2',\n",
       " 'Cocktail': '/m/024g6',\n",
       " 'Computer monitor': '/m/02522',\n",
       " 'Box': '/m/025dyy',\n",
       " 'Stapler': '/m/025fsf',\n",
       " 'Christmas tree': '/m/025nd',\n",
       " 'Cowboy hat': '/m/025rp__',\n",
       " 'Hiking equipment': '/m/0268lbt',\n",
       " 'Studio couch': '/m/026qbn5',\n",
       " 'Drum': '/m/026t6',\n",
       " 'Dessert': '/m/0270h',\n",
       " 'Wine rack': '/m/0271qf7',\n",
       " 'Drink': '/m/0271t',\n",
       " 'Zucchini': '/m/027pcv',\n",
       " 'Ladle': '/m/027rl48',\n",
       " 'Human mouth': '/m/0283dt1',\n",
       " 'Dairy': '/m/0284d',\n",
       " 'Dice': '/m/029b3',\n",
       " 'Oven': '/m/029bxz',\n",
       " 'Dinosaur': '/m/029tx',\n",
       " 'Ratchet': '/m/02bm9n',\n",
       " 'Couch': '/m/02crq1',\n",
       " 'Cricket ball': '/m/02ctlc',\n",
       " 'Winter melon': '/m/02cvgx',\n",
       " 'Spatula': '/m/02d1br',\n",
       " 'Whiteboard': '/m/02d9qx',\n",
       " 'Pencil sharpener': '/m/02ddwp',\n",
       " 'Door': '/m/02dgv',\n",
       " 'Hat': '/m/02dl1y',\n",
       " 'Shower': '/m/02f9f_',\n",
       " 'Eraser': '/m/02fh7f',\n",
       " 'Fedora': '/m/02fq_6',\n",
       " 'Guacamole': '/m/02g30s',\n",
       " 'Dagger': '/m/02gzp',\n",
       " 'Scarf': '/m/02h19r',\n",
       " 'Dolphin': '/m/02hj4',\n",
       " 'Sombrero': '/m/02jfl0',\n",
       " 'Tin can': '/m/02jnhm',\n",
       " 'Mug': '/m/02jvh9',\n",
       " 'Tap': '/m/02jz0l',\n",
       " 'Harbor seal': '/m/02l8p9',\n",
       " 'Stretcher': '/m/02lbcq',\n",
       " 'Can opener': '/m/02mqfb',\n",
       " 'Goggles': '/m/02_n6y',\n",
       " 'Human body': '/m/02p0tk3',\n",
       " 'Roller skates': '/m/02p3w7d',\n",
       " 'Coffee cup': '/m/02p5f1q',\n",
       " 'Cutting board': '/m/02pdsw',\n",
       " 'Blender': '/m/02pjr4',\n",
       " 'Plumbing fixture': '/m/02pkr5',\n",
       " 'Stop sign': '/m/02pv19',\n",
       " 'Office supplies': '/m/02rdsp',\n",
       " 'Volleyball': '/m/02rgn06',\n",
       " 'Vase': '/m/02s195',\n",
       " 'Slow cooker': '/m/02tsc9',\n",
       " 'Wardrobe': '/m/02vkqh8',\n",
       " 'Coffee': '/m/02vqfm',\n",
       " 'Whisk': '/m/02vwcm',\n",
       " 'Paper towel': '/m/02w3r3',\n",
       " 'Personal care': '/m/02w3_ws',\n",
       " 'Food': '/m/02wbm',\n",
       " 'Sun hat': '/m/02wbtzl',\n",
       " 'Tree house': '/m/02wg_p',\n",
       " 'Flying disc': '/m/02wmf',\n",
       " 'Skirt': '/m/02wv6h6',\n",
       " 'Gas stove': '/m/02wv84t',\n",
       " 'Salt and pepper shakers': '/m/02x8cch',\n",
       " 'Mechanical fan': '/m/02x984l',\n",
       " 'Face powder': '/m/02xb7qb',\n",
       " 'Fax': '/m/02xqq',\n",
       " 'Fruit': '/m/02xwb',\n",
       " 'French fries': '/m/02y6n',\n",
       " 'Nightstand': '/m/02z51p',\n",
       " 'Barrel': '/m/02zn6n',\n",
       " 'Kite': '/m/02zt3',\n",
       " 'Tart': '/m/02zvsm',\n",
       " 'Treadmill': '/m/030610',\n",
       " 'Fox': '/m/0306r',\n",
       " 'Flag': '/m/03120',\n",
       " 'Horn': '/m/0319l',\n",
       " 'Window blind': '/m/031b6r',\n",
       " 'Human foot': '/m/031n1',\n",
       " 'Golf cart': '/m/0323sq',\n",
       " 'Jacket': '/m/032b3c',\n",
       " 'Egg': '/m/033cnk',\n",
       " 'Street light': '/m/033rq4',\n",
       " 'Guitar': '/m/0342h',\n",
       " 'Pillow': '/m/034c16',\n",
       " 'Human leg': '/m/035r7c',\n",
       " 'Isopod': '/m/035vxb',\n",
       " 'Grape': '/m/0388q',\n",
       " 'Human ear': '/m/039xj_',\n",
       " 'Power plugs and sockets': '/m/03bbps',\n",
       " 'Panda': '/m/03bj1',\n",
       " 'Giraffe': '/m/03bk1',\n",
       " 'Woman': '/m/03bt1vf',\n",
       " 'Door handle': '/m/03c7gz',\n",
       " 'Rhinoceros': '/m/03d443',\n",
       " 'Bathtub': '/m/03dnzn',\n",
       " 'Goldfish': '/m/03fj2',\n",
       " 'Houseplant': '/m/03fp41',\n",
       " 'Goat': '/m/03fwl',\n",
       " 'Baseball bat': '/m/03g8mr',\n",
       " 'Baseball glove': '/m/03grzl',\n",
       " 'Mixing bowl': '/m/03hj559',\n",
       " 'Marine invertebrates': '/m/03hl4l9',\n",
       " 'Kitchen utensil': '/m/03hlz0c',\n",
       " 'Light switch': '/m/03jbxj',\n",
       " 'House': '/m/03jm5',\n",
       " 'Horse': '/m/03k3r',\n",
       " 'Stationary bicycle': '/m/03kt2w',\n",
       " 'Hammer': '/m/03l9g',\n",
       " 'Ceiling fan': '/m/03ldnb',\n",
       " 'Sofa bed': '/m/03m3pdh',\n",
       " 'Adhesive tape': '/m/03m3vtv',\n",
       " 'Harp': '/m/03m5k',\n",
       " 'Sandal': '/m/03nfch',\n",
       " 'Bicycle helmet': '/m/03p3bw',\n",
       " 'Saucer': '/m/03q5c7',\n",
       " 'Harpsichord': '/m/03q5t',\n",
       " 'Human hair': '/m/03q69',\n",
       " 'Heater': '/m/03qhv5',\n",
       " 'Harmonica': '/m/03qjg',\n",
       " 'Hamster': '/m/03qrc',\n",
       " 'Curtain': '/m/03rszm',\n",
       " 'Bed': '/m/03ssj5',\n",
       " 'Kettle': '/m/03s_tn',\n",
       " 'Fireplace': '/m/03tw93',\n",
       " 'Scale': '/m/03txqz',\n",
       " 'Drinking straw': '/m/03v5tg',\n",
       " 'Insect': '/m/03vt0',\n",
       " 'Hair dryer': '/m/03wvsk',\n",
       " 'Kitchenware': '/m/03_wxk',\n",
       " 'Indoor rower': '/m/03wym',\n",
       " 'Invertebrate': '/m/03xxp',\n",
       " 'Food processor': '/m/03y6mg',\n",
       " 'Bookcase': '/m/03__z0',\n",
       " 'Refrigerator': '/m/040b_t',\n",
       " 'Wood-burning stove': '/m/04169hn',\n",
       " 'Punching bag': '/m/0420v5',\n",
       " 'Common fig': '/m/043nyj',\n",
       " 'Cocktail shaker': '/m/0440zs',\n",
       " 'Jaguar': '/m/0449p',\n",
       " 'Golf ball': '/m/044r5d',\n",
       " 'Fashion accessory': '/m/0463sg',\n",
       " 'Alarm clock': '/m/046dlr',\n",
       " 'Filing cabinet': '/m/047j0r',\n",
       " 'Artichoke': '/m/047v4b',\n",
       " 'Table': '/m/04bcr3',\n",
       " 'Tableware': '/m/04brg2',\n",
       " 'Kangaroo': '/m/04c0y',\n",
       " 'Koala': '/m/04cp_',\n",
       " 'Knife': '/m/04ctx',\n",
       " 'Bottle': '/m/04dr76w',\n",
       " 'Bottle opener': '/m/04f5ws',\n",
       " 'Lynx': '/m/04g2r',\n",
       " 'Lavender': '/m/04gth',\n",
       " 'Lighthouse': '/m/04h7h',\n",
       " 'Dumbbell': '/m/04h8sr',\n",
       " 'Human head': '/m/04hgtk',\n",
       " 'Bowl': '/m/04kkgm',\n",
       " 'Humidifier': '/m/04lvq_',\n",
       " 'Porch': '/m/04m6gz',\n",
       " 'Lizard': '/m/04m9y',\n",
       " 'Billiard table': '/m/04p0qw',\n",
       " 'Mammal': '/m/04rky',\n",
       " 'Mouse': '/m/04rmv',\n",
       " 'Motorcycle': '/m/04_sv',\n",
       " 'Musical instrument': '/m/04szw',\n",
       " 'Swim cap': '/m/04tn4x',\n",
       " 'Frying pan': '/m/04v6l4',\n",
       " 'Snowplow': '/m/04vv5k',\n",
       " 'Bathroom cabinet': '/m/04y4h8h',\n",
       " 'Missile': '/m/04ylt',\n",
       " 'Bust': '/m/04yqq2',\n",
       " 'Man': '/m/04yx4',\n",
       " 'Waffle iron': '/m/04z4wx',\n",
       " 'Milk': '/m/04zpv',\n",
       " 'Ring binder': '/m/04zwwv',\n",
       " 'Plate': '/m/050gv4',\n",
       " 'Mobile phone': '/m/050k8',\n",
       " 'Baked goods': '/m/052lwg6',\n",
       " 'Mushroom': '/m/052sf',\n",
       " 'Crutch': '/m/05441v',\n",
       " 'Pitcher': '/m/054fyh',\n",
       " 'Mirror': '/m/054_l',\n",
       " 'Lifejacket': '/m/054xkw',\n",
       " 'Table tennis racket': '/m/05_5p_0',\n",
       " 'Pencil case': '/m/05676x',\n",
       " 'Musical keyboard': '/m/057cc',\n",
       " 'Scoreboard': '/m/057p5t',\n",
       " 'Briefcase': '/m/0584n8',\n",
       " 'Kitchen knife': '/m/058qzx',\n",
       " 'Nail': '/m/05bm6',\n",
       " 'Tennis ball': '/m/05ctyq',\n",
       " 'Plastic bag': '/m/05gqfk',\n",
       " 'Oboe': '/m/05kms',\n",
       " 'Chest of drawers': '/m/05kyg_',\n",
       " 'Ostrich': '/m/05n4y',\n",
       " 'Piano': '/m/05r5c',\n",
       " 'Girl': '/m/05r655',\n",
       " 'Plant': '/m/05s2s',\n",
       " 'Potato': '/m/05vtc',\n",
       " 'Hair spray': '/m/05w9t9',\n",
       " 'Sports equipment': '/m/05y5lj',\n",
       " 'Pasta': '/m/05z55',\n",
       " 'Penguin': '/m/05z6w',\n",
       " 'Pumpkin': '/m/05zsy',\n",
       " 'Pear': '/m/061_f',\n",
       " 'Infant bed': '/m/061hd_',\n",
       " 'Polar bear': '/m/0633h',\n",
       " 'Mixer': '/m/063rgb',\n",
       " 'Cupboard': '/m/0642b4',\n",
       " 'Jacuzzi': '/m/065h6l',\n",
       " 'Pizza': '/m/0663v',\n",
       " 'Digital clock': '/m/06_72j',\n",
       " 'Pig': '/m/068zj',\n",
       " 'Reptile': '/m/06bt6',\n",
       " 'Rifle': '/m/06c54',\n",
       " 'Lipstick': '/m/06c7f7',\n",
       " 'Skateboard': '/m/06_fw',\n",
       " 'Raven': '/m/06j2d',\n",
       " 'High heels': '/m/06k2mb',\n",
       " 'Red panda': '/m/06l9r',\n",
       " 'Rose': '/m/06m11',\n",
       " 'Rabbit': '/m/06mf6',\n",
       " 'Sculpture': '/m/06msq',\n",
       " 'Saxophone': '/m/06ncr',\n",
       " 'Shotgun': '/m/06nrc',\n",
       " 'Seafood': '/m/06nwz',\n",
       " 'Submarine sandwich': '/m/06pcq',\n",
       " 'Snowboard': '/m/06__v',\n",
       " 'Sword': '/m/06y5r',\n",
       " 'Picture frame': '/m/06z37_',\n",
       " 'Sushi': '/m/07030',\n",
       " 'Loveseat': '/m/0703r8',\n",
       " 'Ski': '/m/071p9',\n",
       " 'Squirrel': '/m/071qp',\n",
       " 'Tripod': '/m/073bxn',\n",
       " 'Stethoscope': '/m/073g6',\n",
       " 'Submarine': '/m/074d1',\n",
       " 'Scorpion': '/m/0755b',\n",
       " 'Segway': '/m/076bq',\n",
       " 'Training bench': '/m/076lb9',\n",
       " 'Snake': '/m/078jl',\n",
       " 'Coffee table': '/m/078n6m',\n",
       " 'Skyscraper': '/m/079cl',\n",
       " 'Sheep': '/m/07bgp',\n",
       " 'Television': '/m/07c52',\n",
       " 'Trombone': '/m/07c6l',\n",
       " 'Tea': '/m/07clx',\n",
       " 'Tank': '/m/07cmd',\n",
       " 'Taco': '/m/07crc',\n",
       " 'Telephone': '/m/07cx4',\n",
       " 'Torch': '/m/07dd4',\n",
       " 'Tiger': '/m/07dm6',\n",
       " 'Strawberry': '/m/07fbm7',\n",
       " 'Trumpet': '/m/07gql',\n",
       " 'Tree': '/m/07j7r',\n",
       " 'Tomato': '/m/07j87',\n",
       " 'Train': '/m/07jdr',\n",
       " 'Tool': '/m/07k1x',\n",
       " 'Picnic basket': '/m/07kng9',\n",
       " 'Cooking spray': '/m/07mcwg',\n",
       " 'Trousers': '/m/07mhn',\n",
       " 'Bowling equipment': '/m/07pj7bq',\n",
       " 'Football helmet': '/m/07qxg_',\n",
       " 'Truck': '/m/07r04',\n",
       " 'Measuring cup': '/m/07v9_z',\n",
       " 'Coffeemaker': '/m/07xyvk',\n",
       " 'Violin': '/m/07y_7',\n",
       " 'Vehicle': '/m/07yv9',\n",
       " 'Handbag': '/m/080hkjn',\n",
       " 'Paper cutter': '/m/080n7g',\n",
       " 'Wine': '/m/081qc',\n",
       " 'Weapon': '/m/083kb',\n",
       " 'Wheel': '/m/083wq',\n",
       " 'Worm': '/m/084hf',\n",
       " 'Wok': '/m/084rd',\n",
       " 'Whale': '/m/084zz',\n",
       " 'Zebra': '/m/0898b',\n",
       " 'Auto part': '/m/08dz3q',\n",
       " 'Jug': '/m/08hvt4',\n",
       " 'Pizza cutter': '/m/08ks85',\n",
       " 'Cream': '/m/08p92x',\n",
       " 'Monkey': '/m/08pbxl',\n",
       " 'Lion': '/m/096mb',\n",
       " 'Bread': '/m/09728',\n",
       " 'Platter': '/m/099ssp',\n",
       " 'Chicken': '/m/09b5t',\n",
       " 'Eagle': '/m/09csl',\n",
       " 'Helicopter': '/m/09ct_',\n",
       " 'Owl': '/m/09d5_',\n",
       " 'Duck': '/m/09ddx',\n",
       " 'Turtle': '/m/09dzg',\n",
       " 'Hippopotamus': '/m/09f20',\n",
       " 'Crocodile': '/m/09f_2',\n",
       " 'Toilet': '/m/09g1w',\n",
       " 'Toilet paper': '/m/09gtd',\n",
       " 'Squid': '/m/09gys',\n",
       " 'Clothing': '/m/09j2d',\n",
       " 'Footwear': '/m/09j5n',\n",
       " 'Lemon': '/m/09k_b',\n",
       " 'Spider': '/m/09kmb',\n",
       " 'Deer': '/m/09kx5',\n",
       " 'Frog': '/m/09ld4',\n",
       " 'Banana': '/m/09qck',\n",
       " 'Rocket': '/m/09rvcxw',\n",
       " 'Wine glass': '/m/09tvcd',\n",
       " 'Countertop': '/m/0b3fp9',\n",
       " 'Tablet computer': '/m/0bh9flk',\n",
       " 'Waste container': '/m/0bjyj5',\n",
       " 'Swimming pool': '/m/0b_rs',\n",
       " 'Dog': '/m/0bt9lr',\n",
       " 'Book': '/m/0bt_c3',\n",
       " 'Elephant': '/m/0bwd_0j',\n",
       " 'Shark': '/m/0by6g',\n",
       " 'Candle': '/m/0c06p',\n",
       " 'Leopard': '/m/0c29q',\n",
       " 'Axe': '/m/0c2jj',\n",
       " 'Hand dryer': '/m/0c3m8g',\n",
       " 'Soap dispenser': '/m/0c3mkw',\n",
       " 'Porcupine': '/m/0c568',\n",
       " 'Flower': '/m/0c9ph5',\n",
       " 'Canary': '/m/0ccs93',\n",
       " 'Cheetah': '/m/0cd4d',\n",
       " 'Palm tree': '/m/0cdl1',\n",
       " 'Hamburger': '/m/0cdn1',\n",
       " 'Maple': '/m/0cffdh',\n",
       " 'Building': '/m/0cgh4',\n",
       " 'Fish': '/m/0ch_cf',\n",
       " 'Lobster': '/m/0cjq5',\n",
       " 'Asparagus': '/m/0cjs7',\n",
       " 'Furniture': '/m/0c_jw',\n",
       " 'Hedgehog': '/m/0cl4p',\n",
       " 'Airplane': '/m/0cmf2',\n",
       " 'Spoon': '/m/0cmx8',\n",
       " 'Otter': '/m/0cn6p',\n",
       " 'Bull': '/m/0cnyhnx',\n",
       " 'Oyster': '/m/0_cp5',\n",
       " 'Horizontal bar': '/m/0cqn2',\n",
       " 'Convenience store': '/m/0crjs',\n",
       " 'Bomb': '/m/0ct4f',\n",
       " 'Bench': '/m/0cvnqh',\n",
       " 'Ice cream': '/m/0cxn2',\n",
       " 'Caterpillar': '/m/0cydv',\n",
       " 'Butterfly': '/m/0cyf8',\n",
       " 'Parachute': '/m/0cyfs',\n",
       " 'Orange': '/m/0cyhj_',\n",
       " 'Antelope': '/m/0czz2',\n",
       " 'Beaker': '/m/0d20w4',\n",
       " 'Moths and butterflies': '/m/0d_2m',\n",
       " 'Window': '/m/0d4v4',\n",
       " 'Closet': '/m/0d4w1',\n",
       " 'Castle': '/m/0d5gx',\n",
       " 'Jellyfish': '/m/0d8zb',\n",
       " 'Goose': '/m/0dbvp',\n",
       " 'Mule': '/m/0dbzx',\n",
       " 'Swan': '/m/0dftk',\n",
       " 'Peach': '/m/0dj6p',\n",
       " 'Coconut': '/m/0djtd',\n",
       " 'Seat belt': '/m/0dkzw',\n",
       " 'Raccoon': '/m/0dq75',\n",
       " 'Chisel': '/m/0_dqb',\n",
       " 'Fork': '/m/0dt3t',\n",
       " 'Lamp': '/m/0dtln',\n",
       " 'Camera': '/m/0dv5r',\n",
       " 'Squash': '/m/0dv77',\n",
       " 'Racket': '/m/0dv9c',\n",
       " 'Human face': '/m/0dzct',\n",
       " 'Human arm': '/m/0dzf4',\n",
       " 'Vegetable': '/m/0f4s2w',\n",
       " 'Diaper': '/m/0f571',\n",
       " 'Unicycle': '/m/0f6nr',\n",
       " 'Falcon': '/m/0f6wt',\n",
       " 'Chime': '/m/0f8s22',\n",
       " 'Snail': '/m/0f9_l',\n",
       " 'Shellfish': '/m/0fbdv',\n",
       " 'Cabbage': '/m/0fbw6',\n",
       " 'Carrot': '/m/0fj52s',\n",
       " 'Mango': '/m/0fldg',\n",
       " 'Jeans': '/m/0fly7',\n",
       " 'Flowerpot': '/m/0fm3zh',\n",
       " 'Pineapple': '/m/0fp6w',\n",
       " 'Drawer': '/m/0fqfqc',\n",
       " 'Stool': '/m/0fqt361',\n",
       " 'Envelope': '/m/0frqm',\n",
       " 'Cake': '/m/0fszt',\n",
       " 'Dragonfly': '/m/0ft9s',\n",
       " 'Sunflower': '/m/0ftb8',\n",
       " 'Microwave oven': '/m/0fx9l',\n",
       " 'Honeycomb': '/m/0fz0h',\n",
       " 'Marine mammal': '/m/0gd2v',\n",
       " 'Sea lion': '/m/0gd36',\n",
       " 'Ladybug': '/m/0gj37',\n",
       " 'Shelf': '/m/0gjbg72',\n",
       " 'Watch': '/m/0gjkl',\n",
       " 'Candy': '/m/0gm28',\n",
       " 'Salad': '/m/0grw1',\n",
       " 'Parrot': '/m/0gv1x',\n",
       " 'Handgun': '/m/0gxl3',\n",
       " 'Sparrow': '/m/0h23m',\n",
       " 'Van': '/m/0h2r6',\n",
       " 'Grinder': '/m/0h8jyh6',\n",
       " 'Spice rack': '/m/0h8kx63',\n",
       " 'Light bulb': '/m/0h8l4fh',\n",
       " 'Corded phone': '/m/0h8lkj8',\n",
       " 'Sports uniform': '/m/0h8mhzd',\n",
       " 'Tennis racket': '/m/0h8my_4',\n",
       " 'Wall clock': '/m/0h8mzrc',\n",
       " 'Serving tray': '/m/0h8n27j',\n",
       " 'Kitchen & dining room table': '/m/0h8n5zk',\n",
       " 'Dog bed': '/m/0h8n6f9',\n",
       " 'Cake stand': '/m/0h8n6ft',\n",
       " 'Cat furniture': '/m/0h8nm9j',\n",
       " 'Bathroom accessory': '/m/0h8nr_l',\n",
       " 'Facial tissue holder': '/m/0h8nsvg',\n",
       " 'Pressure cooker': '/m/0h8ntjv',\n",
       " 'Kitchen appliance': '/m/0h99cwc',\n",
       " 'Tire': '/m/0h9mv',\n",
       " 'Ruler': '/m/0hdln',\n",
       " 'Luggage and bags': '/m/0hf58v5',\n",
       " 'Microphone': '/m/0hg7b',\n",
       " 'Broccoli': '/m/0hkxq',\n",
       " 'Umbrella': '/m/0hnnb',\n",
       " 'Pastry': '/m/0hnyx',\n",
       " 'Grapefruit': '/m/0hqkz',\n",
       " 'Band-aid': '/m/0j496',\n",
       " 'Animal': '/m/0jbk',\n",
       " 'Bell pepper': '/m/0jg57',\n",
       " 'Turkey': '/m/0jly1',\n",
       " 'Lily': '/m/0jqgx',\n",
       " 'Pomegranate': '/m/0jwn_',\n",
       " 'Doughnut': '/m/0jy4k',\n",
       " 'Glasses': '/m/0jyfg',\n",
       " 'Human nose': '/m/0k0pj',\n",
       " 'Pen': '/m/0k1tl',\n",
       " 'Ant': '/m/0_k2',\n",
       " 'Car': '/m/0k4j',\n",
       " 'Aircraft': '/m/0k5j',\n",
       " 'Human hand': '/m/0k65p',\n",
       " 'Skunk': '/m/0km7z',\n",
       " 'Teddy bear': '/m/0kmg4',\n",
       " 'Watermelon': '/m/0kpqd',\n",
       " 'Cantaloupe': '/m/0kpt_',\n",
       " 'Dishwasher': '/m/0ky7b',\n",
       " 'Flute': '/m/0l14j_',\n",
       " 'Balance beam': '/m/0l3ms',\n",
       " 'Sandwich': '/m/0l515',\n",
       " 'Shrimp': '/m/0ll1f78',\n",
       " 'Sewing machine': '/m/0llzx',\n",
       " 'Binoculars': '/m/0lt4_',\n",
       " 'Rays and skates': '/m/0m53l',\n",
       " 'Ipod': '/m/0mcx2',\n",
       " 'Accordion': '/m/0mkg',\n",
       " 'Willow': '/m/0mw_6',\n",
       " 'Crab': '/m/0n28_',\n",
       " 'Crown': '/m/0nl46',\n",
       " 'Seahorse': '/m/0nybt',\n",
       " 'Perfume': '/m/0p833',\n",
       " 'Alpaca': '/m/0pcr',\n",
       " 'Taxi': '/m/0pg52',\n",
       " 'Canoe': '/m/0ph39',\n",
       " 'Remote control': '/m/0qjjc',\n",
       " 'Wheelchair': '/m/0qmmr',\n",
       " 'Rugby ball': '/m/0wdt60w',\n",
       " 'Armadillo': '/m/0xfy',\n",
       " 'Maracas': '/m/0xzly',\n",
       " 'Helmet': '/m/0zvk5'}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import csv\n",
    "with open(\"class-descriptions-boxable.csv\") as file:\n",
    "    reader = csv.reader(file)\n",
    "    dict_list = {row[1]:row[0] for row in reader}\n",
    "dict_list"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}