dataset_test-tk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T17:21:53.763443Z",
     "start_time": "2020-03-10T17:21:52.111164Z"
    }
   },
   "outputs": [],
   "source": [
    "from pipeline.load import MyDataset, get_dataloader\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import requests\n",
    "import pandas as pd\n",
    "import json\n",
    "import os\n",
    "import pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T17:22:58.302533Z",
     "start_time": "2020-03-10T17:22:58.227251Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training_data/train\n",
      "training_data/train/images/*.jpg\n"
     ]
    }
   ],
   "source": [
    "dataset = MyDataset(in_dir=\"training_data/train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T17:23:01.637504Z",
     "start_time": "2020-03-10T17:23:01.633175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['training_data/train/images/d41d81_14336_19456_i.jpg',\n",
       " 'training_data/train/images/d41d81_1024_21504_i.jpg',\n",
       " 'training_data/train/images/d41d81_20480_20480_i.jpg',\n",
       " 'training_data/train/images/d41d81_31744_15360_i.jpg',\n",
       " 'training_data/train/images/d41d81_24576_10240_i.jpg']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.images[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T17:23:06.142090Z",
     "start_time": "2020-03-10T17:23:06.137711Z"
    }
   },
   "outputs": [],
   "source": [
    "def display_random(image_tuple=None):\n",
    "    if image_tuple==None:\n",
    "        image_tuple=dataset[np.random.randint(0, len(dataset))]\n",
    "    fig, ax = plt.subplots(ncols=len(image_tuple)-1)\n",
    "    ax[0].imshow(image_tuple[0])\n",
    "    ax[1].imshow(image_tuple[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T17:23:13.355749Z",
     "start_time": "2020-03-10T17:23:13.098739Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ls: cannot access 'training_data/train/images': No such file or directory\n",
      "0\n",
      "ls: cannot access 'training_data/train/masks': No such file or directory\n",
      "0\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Study Train / Test File sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T17:23:45.329143Z",
     "start_time": "2020-03-10T17:23:45.175811Z"
    }
   },
   "outputs": [],
   "source": [
    "scene_log = requests.get('https://raw.githubusercontent.com/thomkav/building_segmentation_cv/master/scene_log.json').json()\n",
    "scene_log = pd.DataFrame.from_dict(scene_log,orient='index')\n",
    "scene_log['num_blocks'] = scene_log['blocks']\n",
    "scene_log.size = pd.to_numeric(scene_log.size)\n",
    "scene_log.num_blocks = pd.to_numeric(scene_log.num_blocks)\n",
    "scene_log.tier = pd.to_numeric(scene_log.tier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-10T17:23:32.908155Z",
     "start_time": "2020-03-10T17:23:32.897260Z"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'scene_log' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-e611628da82e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscene_log\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mscene_log\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtier\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscene_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'scene_log' is not defined"
     ]
    }
   ],
   "source": [
    "scene_log[scene_log.tier == 2].scene_id.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = set(sm.index.values)\n",
    "train_regions = {'dar','kam','mon','nia','ptn','znz'}\n",
    "test_regions = cities - train_regions\n",
    "\n",
    "\n",
    "train_size = sm[sm.index.isin(train_regions)].size.sum()\n",
    "train_num_blocks = sm[sm.index.isin(train_regions)].num_blocks.sum()\n",
    "\n",
    "print(f'{len(train_regions)} of {len(cities)} cities used for train region')\n",
    "print(f'Training regs are {round(train_size / sm.size.sum(),2)*100}% of total area')\n",
    "print(f'Training regs are {round(train_num_blocks / sm.num_blocks.sum(), 2)*100}% of unique blocks')\n",
    "train_sc_ids = sm[sm.index.isin(train_regions)].scene_id.unique()\n",
    "test_sc_ids = sm[~sm.index.isin(train_regions)].scene_id.unique()\n",
    "print('Train scene ID list:', train_sc_ids)\n",
    "print('Test scene ID list:', test_sc_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tile Counts per Scene for validation split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "compress_submission_data.py  Model Inspection-Copy1.ipynb  README.md\r\n",
      "dataset_test-Copy1.ipynb     Model Inspection.ipynb\t   scene_log.json\r\n",
      "dataset_test.ipynb\t     models\t\t\t   submission_data\r\n",
      "environment.yml\t\t     nohup.out\t\t\t   tmp\r\n",
      "evaluate.py\t\t     pipeline\t\t\t   training_data\r\n",
      "FastFCN\t\t\t     project_mgmt\t\t   train.py\r\n",
      "LovaszSoftmax\t\t     __pycache__\t\t   Untitled.ipynb\r\n"
     ]
    }
   ],
   "source": [
    "!ls "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       " 'a42435_15360_32768_i.jpg',\n",
       " '3b20d4_8192_31744_i.jpg',\n",
       " '425403_3072_1024_i.jpg',\n",
       " '42f235_35840_26624_i.jpg',\n",
       " '665946_78848_82944_i.jpg',\n",
       " 'a42435_30720_46080_i.jpg',\n",
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       " 'f883a0_39936_8192_i.jpg',\n",
       " '665946_94208_75776_i.jpg',\n",
       " '665946_51200_7168_i.jpg',\n",
       " '06f252_41984_2048_i.jpg',\n",
       " 'bd5c14_9216_23552_i.jpg',\n",
       " 'a017f9_21504_11264_i.jpg',\n",
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       " '493701_16384_13312_i.jpg',\n",
       " 'ca041a_28672_38912_i.jpg',\n",
       " 'd41d81_31744_20480_i.jpg',\n",
       " '4e7c7f_15360_15360_i.jpg',\n",
       " 'bc32f1_8192_20480_i.jpg',\n",
       " '665946_78848_80896_i.jpg',\n",
       " '3b20d4_29696_10240_i.jpg',\n",
       " '401175_6144_14336_i.jpg',\n",
       " 'c7415c_27648_10240_i.jpg',\n",
       " 'f883a0_15360_38912_i.jpg',\n",
       " '06f252_0_13312_i.jpg',\n",
       " '3f8360_3072_19456_i.jpg',\n",
       " '33cae6_5120_27648_i.jpg',\n",
       " 'f883a0_48128_2048_i.jpg',\n",
       " '9b8638_22528_5120_i.jpg',\n",
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       " 'f883a0_22528_50176_i.jpg',\n",
       " '33cae6_11264_12288_i.jpg',\n",
       " 'a017f9_11264_25600_i.jpg',\n",
       " '0a4c40_1024_11264_i.jpg',\n",
       " 'aee7fd_3072_9216_i.jpg',\n",
       " '75cdfa_44032_44032_i.jpg',\n",
       " 'a42435_18432_52224_i.jpg',\n",
       " 'f883a0_22528_1024_i.jpg',\n",
       " 'b15fce_23552_34816_i.jpg',\n",
       " '75cdfa_6144_6144_i.jpg',\n",
       " '353093_38912_21504_i.jpg',\n",
       " 'a42435_3072_20480_i.jpg',\n",
       " '42f235_2048_30720_i.jpg',\n",
       " '0a4c40_45056_15360_i.jpg',\n",
       " 'f883a0_22528_8192_i.jpg',\n",
       " '33cae6_32768_26624_i.jpg',\n",
       " 'a017f9_47104_46080_i.jpg',\n",
       " 'c7415c_9216_14336_i.jpg',\n",
       " 'a017f9_35840_44032_i.jpg',\n",
       " '665946_33792_22528_i.jpg',\n",
       " '665946_69632_66560_i.jpg',\n",
       " 'bd5c14_26624_6144_i.jpg',\n",
       " 'f883a0_59392_46080_i.jpg',\n",
       " 'bc32f1_32768_30720_i.jpg',\n",
       " 'f883a0_3072_12288_i.jpg',\n",
       " 'a42435_12288_34816_i.jpg',\n",
       " 'bc32f1_3072_5120_i.jpg',\n",
       " '665946_79872_70656_i.jpg',\n",
       " '0a4c40_30720_5120_i.jpg',\n",
       " '665946_10240_33792_i.jpg',\n",
       " 'f883a0_57344_4096_i.jpg',\n",
       " 'bc32f1_4096_23552_i.jpg',\n",
       " '4e7c7f_8192_35840_i.jpg',\n",
       " 'a017f9_5120_23552_i.jpg',\n",
       " '207cc7_10240_17408_i.jpg',\n",
       " 'f883a0_53248_39936_i.jpg',\n",
       " '665946_16384_28672_i.jpg',\n",
       " 'bc32f1_13312_16384_i.jpg',\n",
       " 'a017f9_38912_43008_i.jpg',\n",
       " '076995_17408_1024_i.jpg',\n",
       " 'f883a0_5120_26624_i.jpg',\n",
       " 'aee7fd_10240_29696_i.jpg',\n",
       " '75cdfa_6144_11264_i.jpg',\n",
       " '076995_18432_21504_i.jpg',\n",
       " '42f235_0_23552_i.jpg',\n",
       " '0a4c40_16384_15360_i.jpg',\n",
       " '425403_27648_29696_i.jpg',\n",
       " '3b20d4_33792_39936_i.jpg',\n",
       " '06f252_33792_18432_i.jpg',\n",
       " '665946_100352_61440_i.jpg',\n",
       " 'e52478_9216_4096_i.jpg',\n",
       " '076995_11264_37888_i.jpg',\n",
       " '076995_39936_0_i.jpg',\n",
       " '076995_19456_14336_i.jpg',\n",
       " '75cdfa_24576_12288_i.jpg',\n",
       " 'f883a0_27648_39936_i.jpg',\n",
       " '42f235_36864_36864_i.jpg',\n",
       " 'ca041a_41984_23552_i.jpg',\n",
       " '665946_94208_76800_i.jpg',\n",
       " 'bc32f1_31744_37888_i.jpg',\n",
       " 'a42435_3072_11264_i.jpg',\n",
       " '076995_34816_14336_i.jpg',\n",
       " '3f8360_31744_11264_i.jpg',\n",
       " '353093_4096_6144_i.jpg',\n",
       " '0a4c40_25600_21504_i.jpg',\n",
       " '665946_36864_31744_i.jpg',\n",
       " 'f883a0_19456_17408_i.jpg',\n",
       " '9b8638_31744_17408_i.jpg',\n",
       " '493701_16384_4096_i.jpg',\n",
       " 'ca041a_21504_55296_i.jpg',\n",
       " 'f883a0_26624_63488_i.jpg',\n",
       " '665946_130048_63488_i.jpg',\n",
       " '3f8360_36864_1024_i.jpg',\n",
       " 'aee7fd_21504_36864_i.jpg',\n",
       " 'bd5c14_12288_31744_i.jpg',\n",
       " 'a42435_27648_34816_i.jpg',\n",
       " '076995_25600_19456_i.jpg',\n",
       " '42f235_14336_39936_i.jpg',\n",
       " '4e7c7f_19456_12288_i.jpg',\n",
       " '75cdfa_41984_21504_i.jpg',\n",
       " '3f8360_24576_10240_i.jpg',\n",
       " 'b15fce_16384_27648_i.jpg',\n",
       " '353093_2048_10240_i.jpg',\n",
       " '4e7c7f_33792_22528_i.jpg',\n",
       " '42f235_4096_2048_i.jpg',\n",
       " 'a42435_6144_37888_i.jpg',\n",
       " 'bd5c14_26624_15360_i.jpg',\n",
       " '353093_28672_37888_i.jpg',\n",
       " '665946_14336_41984_i.jpg',\n",
       " '9b8638_36864_8192_i.jpg',\n",
       " '3b20d4_36864_2048_i.jpg',\n",
       " '75cdfa_25600_1024_i.jpg',\n",
       " '665946_44032_70656_i.jpg',\n",
       " 'a42435_1024_44032_i.jpg',\n",
       " '665946_89088_56320_i.jpg',\n",
       " 'c7415c_38912_13312_i.jpg',\n",
       " 'b15fce_27648_36864_i.jpg',\n",
       " '75cdfa_33792_1024_i.jpg',\n",
       " '665946_24576_68608_i.jpg',\n",
       " 'bc32f1_23552_31744_i.jpg',\n",
       " '665946_69632_64512_i.jpg',\n",
       " '75cdfa_23552_21504_i.jpg',\n",
       " 'f883a0_47104_30720_i.jpg',\n",
       " '0a4c40_30720_36864_i.jpg',\n",
       " 'a42435_25600_25600_i.jpg',\n",
       " '06f252_15360_19456_i.jpg',\n",
       " '665946_3072_34816_i.jpg',\n",
       " 'e52478_29696_9216_i.jpg',\n",
       " '665946_145408_72704_i.jpg',\n",
       " 'f883a0_36864_22528_i.jpg',\n",
       " '06f252_20480_22528_i.jpg',\n",
       " '665946_22528_44032_i.jpg',\n",
       " '665946_20480_71680_i.jpg',\n",
       " '665946_59392_64512_i.jpg',\n",
       " '0a4c40_23552_25600_i.jpg',\n",
       " 'bd5c14_6144_25600_i.jpg',\n",
       " '42f235_45056_33792_i.jpg',\n",
       " 'e52478_10240_6144_i.jpg',\n",
       " 'd41d81_18432_16384_i.jpg',\n",
       " 'f883a0_9216_2048_i.jpg',\n",
       " ...]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls = os.listdir('training_data/images')\n",
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter, defaultdict\n",
    "import re\n",
    "\n",
    "ids = [fn[:6] for fn in ls]\n",
    "counts = Counter(ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'076995': 1701,\n",
       "         '75cdfa': 2304,\n",
       "         '353093': 1364,\n",
       "         '425403': 1444,\n",
       "         'f883a0': 4094,\n",
       "         '665946': 6982,\n",
       "         'a42435': 1318,\n",
       "         '33cae6': 1106,\n",
       "         '0a4c40': 1604,\n",
       "         '06f252': 2387,\n",
       "         '42f235': 2257,\n",
       "         'abe1a3': 31,\n",
       "         'a017f9': 1642,\n",
       "         'e52478': 601,\n",
       "         'c7415c': 1346,\n",
       "         'bc32f1': 1521,\n",
       "         'b15fce': 1395,\n",
       "         '3f8360': 1086,\n",
       "         'aee7fd': 1521,\n",
       "         '4e7c7f': 868,\n",
       "         'ca041a': 1566,\n",
       "         '9b8638': 1482,\n",
       "         '401175': 192,\n",
       "         'd41d81': 737,\n",
       "         'bd5c14': 1369,\n",
       "         '3b20d4': 1849,\n",
       "         '493701': 280,\n",
       "         'f15272': 205,\n",
       "         '825a50': 65,\n",
       "         '207cc7': 126,\n",
       "         'f49f31': 13})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'665946': {'scene_id': '665946',\n",
       "  'shape': [150147, 84466],\n",
       "  'size': '12682316502',\n",
       "  'blocks': '12043',\n",
       "  'lnglat': '(-0.23537189476772363, 5.633677745875293)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_1/acc/665946/665946.tif',\n",
       "  'city': 'acc',\n",
       "  'tier': '1',\n",
       "  'res': [0.02001518707102818, 0.02001518707102818]},\n",
       " 'a42435': {'scene_id': 'a42435',\n",
       "  'shape': [39162, 57540],\n",
       "  'size': '2253381480',\n",
       "  'blocks': '2135',\n",
       "  'lnglat': '(-0.24157741106535752, 5.6135225842052705)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_1/acc/a42435/a42435.tif',\n",
       "  'city': 'acc',\n",
       "  'tier': '1',\n",
       "  'res': [0.032029411960186015, 0.03202926727370731]},\n",
       " 'ca041a': {'scene_id': 'ca041a',\n",
       "  'shape': [77778, 65882],\n",
       "  'size': '5124170196',\n",
       "  'blocks': '4825',\n",
       "  'lnglat': '(-0.21648728126184752, 5.598111242040536)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_1/acc/ca041a/ca041a.tif',\n",
       "  'city': 'acc',\n",
       "  'tier': '1',\n",
       "  'res': [0.035820209694930036, 0.035820613560994544]},\n",
       " 'd41d81': {'scene_id': 'd41d81',\n",
       "  'shape': [42719, 40868],\n",
       "  'size': '1745840092',\n",
       "  'blocks': '1636',\n",
       "  'lnglat': '(-0.19908004925217568, 5.583252127510706)',\n",
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       " 'ca3445': {'scene_id': 'ca3445',\n",
       "  'shape': [39048, 48188],\n",
       "  'size': '1881645024',\n",
       "  'blocks': '1788',\n",
       "  'lnglat': '(39.26101957085713, -6.822255855401258)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/ca3445/ca3445.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.041140000000002806, 0.041140000000006456]},\n",
       " '82a1f3': {'scene_id': '82a1f3',\n",
       "  'shape': [28182, 49331],\n",
       "  'size': '1390246242',\n",
       "  'blocks': '1300',\n",
       "  'lnglat': '(39.26122558457094, -6.809745175414554)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/82a1f3/82a1f3.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.0411399999999973, 0.04113999999998534]},\n",
       " 'cf83de': {'scene_id': 'cf83de',\n",
       "  'shape': [38108, 47909],\n",
       "  'size': '1825716172',\n",
       "  'blocks': '1731',\n",
       "  'lnglat': '(39.2431220659347, -6.807908007210717)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/cf83de/cf83de.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.04113999999999339, 0.04113999999999339]},\n",
       " '97ce35': {'scene_id': '97ce35',\n",
       "  'shape': [37580, 35065],\n",
       "  'size': '1317742700',\n",
       "  'blocks': '1232',\n",
       "  'lnglat': '(39.21570025026928, -6.738052366584108)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/97ce35/97ce35.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07538, 0.07538]},\n",
       " '24a7d8': {'scene_id': '24a7d8',\n",
       "  'shape': [39468, 35281],\n",
       "  'size': '1392470508',\n",
       "  'blocks': '1309',\n",
       "  'lnglat': '(39.2336128664397, -6.738034022973589)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/24a7d8/24a7d8.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07163, 0.07163]},\n",
       " 'b8faa3': {'scene_id': 'b8faa3',\n",
       "  'shape': [46813, 31394],\n",
       "  'size': '1469647322',\n",
       "  'blocks': '1379',\n",
       "  'lnglat': '(39.217272079610424, -6.76230063320355)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/b8faa3/b8faa3.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07512, 0.07512]},\n",
       " '63c3f9': {'scene_id': '63c3f9',\n",
       "  'shape': [47239, 49766],\n",
       "  'size': '2350896074',\n",
       "  'blocks': '2235',\n",
       "  'lnglat': '(39.23845054691127, -6.761943916381007)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/63c3f9/63c3f9.tif',\n",
       "  'city': 'dar',\n",
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       " 'c533fa': {'scene_id': 'c533fa',\n",
       "  'shape': [35216, 51644],\n",
       "  'size': '1818695104',\n",
       "  'blocks': '1714',\n",
       "  'lnglat': '(39.265503135307284, -6.766242004820011)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/c533fa/c533fa.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07121000000000001, 0.07121000000000001]},\n",
       " '5fe6fb': {'scene_id': '5fe6fb',\n",
       "  'shape': [46756, 48776],\n",
       "  'size': '2280570656',\n",
       "  'blocks': '2143',\n",
       "  'lnglat': '(39.21155202824212, -6.789451536812495)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/5fe6fb/5fe6fb.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07351, 0.07351]},\n",
       " '385a0e': {'scene_id': '385a0e',\n",
       "  'shape': [49240, 50881],\n",
       "  'size': '2505380440',\n",
       "  'blocks': '2385',\n",
       "  'lnglat': '(39.23857682069573, -6.78934606866768)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/385a0e/385a0e.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07213, 0.07213]},\n",
       " '759e34': {'scene_id': '759e34',\n",
       "  'shape': [49277, 20553],\n",
       "  'size': '1012790181',\n",
       "  'blocks': '963',\n",
       "  'lnglat': '(39.28284734550224, -6.788743611168467)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/759e34/759e34.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07250000000000001, 0.07250000000000001]},\n",
       " 'ef8f27': {'scene_id': 'ef8f27',\n",
       "  'shape': [48185, 50259],\n",
       "  'size': '2421729915',\n",
       "  'blocks': '2306',\n",
       "  'lnglat': '(39.211233198923075, -6.816158673829341)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/ef8f27/ef8f27.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07358, 0.07358]},\n",
       " '1d8af6': {'scene_id': '1d8af6',\n",
       "  'shape': [46518, 49159],\n",
       "  'size': '2286778362',\n",
       "  'blocks': '2160',\n",
       "  'lnglat': '(39.23820487725842, -6.816434498116066)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/1d8af6/1d8af6.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07525000000000001, 0.07525000000000001]},\n",
       " 'd2f2f4': {'scene_id': 'd2f2f4',\n",
       "  'shape': [48567, 57908],\n",
       "  'size': '2812417836',\n",
       "  'blocks': '2657',\n",
       "  'lnglat': '(39.23671265541112, -6.8433964183535485)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/d2f2f4/d2f2f4.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07288, 0.07288]},\n",
       " 'eadfa3': {'scene_id': 'eadfa3',\n",
       "  'shape': [48289, 51566],\n",
       "  'size': '2490070574',\n",
       "  'blocks': '2366',\n",
       "  'lnglat': '(39.26581485788931, -6.843413664726907)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/eadfa3/eadfa3.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07239000000000001, 0.07239000000000001]},\n",
       " 'cbf72d': {'scene_id': 'cbf72d',\n",
       "  'shape': [48650, 39625],\n",
       "  'size': '1927756250',\n",
       "  'blocks': '1818',\n",
       "  'lnglat': '(39.28962189276701, -6.843700535498471)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/cbf72d/cbf72d.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07352, 0.07352]},\n",
       " '5fadcd': {'scene_id': '5fadcd',\n",
       "  'shape': [43339, 78103],\n",
       "  'size': '3384905917',\n",
       "  'blocks': '3203',\n",
       "  'lnglat': '(39.267117106257906, -6.869015230654086)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/5fadcd/5fadcd.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.0722, 0.0722]},\n",
       " '56e713': {'scene_id': '56e713',\n",
       "  'shape': [74840, 31511],\n",
       "  'size': '2358283240',\n",
       "  'blocks': '2246',\n",
       "  'lnglat': '(39.269588739407176, -6.900838035708066)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/56e713/56e713.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07968, 0.07968]},\n",
       " '219237': {'scene_id': '219237',\n",
       "  'shape': [48119, 86638],\n",
       "  'size': '4168933922',\n",
       "  'blocks': '3891',\n",
       "  'lnglat': '(39.16252481370719, -6.881065874720147)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/219237/219237.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07356, 0.07356]},\n",
       " '109874': {'scene_id': '109874',\n",
       "  'shape': [63666, 84738],\n",
       "  'size': '5394929508',\n",
       "  'blocks': '5130',\n",
       "  'lnglat': '(39.20931003813529, -6.8608302749794765)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/109874/109874.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07178000000000001, 0.07178000000000001]},\n",
       " 'f9d120': {'scene_id': 'f9d120',\n",
       "  'shape': [31840, 80261],\n",
       "  'size': '2555510240',\n",
       "  'blocks': '2429',\n",
       "  'lnglat': '(39.17909993063278, -6.7888554663580285)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/f9d120/f9d120.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07432000000000001, 0.07432000000000001]},\n",
       " '541eff': {'scene_id': '541eff',\n",
       "  'shape': [28334, 25885],\n",
       "  'size': '733425590',\n",
       "  'blocks': '682',\n",
       "  'lnglat': '(39.27150869027245, -6.931422127476599)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/dar/541eff/541eff.tif',\n",
       "  'city': 'dar',\n",
       "  'tier': '2',\n",
       "  'res': [0.07521000000000001, 0.07521000000000001]},\n",
       " '4f38e1': {'scene_id': '4f38e1',\n",
       "  'shape': [59802, 56883],\n",
       "  'size': '3401717166',\n",
       "  'blocks': '3221',\n",
       "  'lnglat': '(13.594722537041129, 7.334282323717521)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/gao/4f38e1/4f38e1.tif',\n",
       "  'city': 'gao',\n",
       "  'tier': '2',\n",
       "  'res': [0.04572649999999983, 0.04572649999999983]},\n",
       " '048ffb': {'scene_id': '048ffb',\n",
       "  'shape': [53447, 62329],\n",
       "  'size': '3331298063',\n",
       "  'blocks': '3165',\n",
       "  'lnglat': '(13.562391956341967, 7.311625533712931)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/gao/048ffb/048ffb.tif',\n",
       "  'city': 'gao',\n",
       "  'tier': '2',\n",
       "  'res': [0.04769530000000012, 0.04769530000000012]},\n",
       " '71e6c2': {'scene_id': '71e6c2',\n",
       "  'shape': [91616, 52517],\n",
       "  'size': '4811397472',\n",
       "  'blocks': '4564',\n",
       "  'lnglat': '(55.45986601643745, -4.621536875590944)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/mah/71e6c2/71e6c2.tif',\n",
       "  'city': 'mah',\n",
       "  'tier': '2',\n",
       "  'res': [0.06079000000000276, 0.06079000000000276]},\n",
       " 'bd129c': {'scene_id': 'bd129c',\n",
       "  'shape': [30556, 38044],\n",
       "  'size': '1162472464',\n",
       "  'blocks': '1077',\n",
       "  'lnglat': '(55.480798963360456, -4.7591203857366)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/mah/bd129c/bd129c.tif',\n",
       "  'city': 'mah',\n",
       "  'tier': '2',\n",
       "  'res': [0.06465999999999543, 0.06465999999999543]},\n",
       " '240168': {'scene_id': '240168',\n",
       "  'shape': [48807, 33794],\n",
       "  'size': '1649383758',\n",
       "  'blocks': '1551',\n",
       "  'lnglat': '(55.52244480021075, -4.700789389706593)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/mah/240168/240168.tif',\n",
       "  'city': 'mah',\n",
       "  'tier': '2',\n",
       "  'res': [0.06522999999998413, 0.06522999999998413]},\n",
       " '5fe2d3': {'scene_id': '5fe2d3',\n",
       "  'shape': [36582, 30428],\n",
       "  'size': '1113117096',\n",
       "  'blocks': '1040',\n",
       "  'lnglat': '(55.51471637326165, -4.744313224748016)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/mah/5fe2d3/5fe2d3.tif',\n",
       "  'city': 'mah',\n",
       "  'tier': '2',\n",
       "  'res': [0.06583999999999124, 0.06583999999999124]},\n",
       " '10d200': {'scene_id': '10d200',\n",
       "  'shape': [4637, 7192],\n",
       "  'size': '33349304',\n",
       "  'blocks': '28',\n",
       "  'lnglat': '(15.336628395901105, -4.412474716894297)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/kin/10d200/10d200.tif',\n",
       "  'city': 'kin',\n",
       "  'tier': '2',\n",
       "  'res': [0.12649999999994527, 0.12649999999994527]},\n",
       " '255028': {'scene_id': '255028',\n",
       "  'shape': [6387, 3207],\n",
       "  'size': '20483109',\n",
       "  'blocks': '18',\n",
       "  'lnglat': '(15.362613770386796, -4.3911722710625805)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/kin/255028/255028.tif',\n",
       "  'city': 'kin',\n",
       "  'tier': '2',\n",
       "  'res': [0.1729999999999265, 0.1729999999999265]},\n",
       " '982a1f': {'scene_id': '982a1f',\n",
       "  'shape': [27395, 20397],\n",
       "  'size': '558775815',\n",
       "  'blocks': '517',\n",
       "  'lnglat': '(2.1426171105023064, 13.470467637221411)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/nia/982a1f/982a1f.tif',\n",
       "  'city': 'nia',\n",
       "  'tier': '2',\n",
       "  'res': [0.058889962334770014, 0.058889962334770014]},\n",
       " 'b80615': {'scene_id': 'b80615',\n",
       "  'shape': [18721, 18331],\n",
       "  'size': '343174651',\n",
       "  'blocks': '322',\n",
       "  'lnglat': '(2.1418785169221066, 13.470132873613347)',\n",
       "  'scene_url': 'https://drivendata-competition-building-segmentation.s3-us-west-1.amazonaws.com/train_tier_2/nia/b80615/b80615.tif',\n",
       "  'city': 'nia',\n",
       "  'tier': '2',\n",
       "  'res': [0.10138416705934485, 0.10138416705934485]}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scene_log = requests.get('https://raw.githubusercontent.com/thomkav/building_segmentation_cv/master/scene_log.json').json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tier 1 scenes\n",
      "{'401175', '4e7c7f', 'abe1a3', '0a4c40', 'aee7fd', 'c7415c', '06f252', '076995', 'ca041a', '33cae6', '3b20d4', '207cc7', 'b15fce', '425403', 'a42435', '665946', 'bd5c14', '9b8638', '493701', '353093', 'bc32f1', 'd41d81', '42f235', 'f15272', 'a017f9', 'f49f31', '825a50', 'f883a0', 'e52478', '75cdfa', '3f8360'}\n",
      "tier 2 scenes\n",
      "{'4f38e1', 'e14d1d', '63c3f9', '5fadcd', '1d8af6', 'c533fa', '541eff', 'b80615', '5fe2d3', '982a1f', 'ab32c9', '8737a8', '9870ba', '240168', '3b3e53', '0ccd08', '219237', 'f9d120', 'ca3445', '759e34', '048ffb', '24a7d8', 'eadfa3', '97ce35', 'b8faa3', 'cf83de', '56e713', '109874', '255028', '82a1f3', 'ef8f27', '5fe6fb', 'bd129c', 'd2f2f4', '8d7dd4', '10d200', 'fb4c1a', '94a004', '385a0e', '71e6c2', 'cbf72d'}\n"
     ]
    }
   ],
   "source": [
    "tier_1_scenes = []\n",
    "tier_2_scenes = []\n",
    "for scene_id, values in scene_log.items():\n",
    "    if values['tier'] == '1':\n",
    "        tier_1_scenes.append(scene_id)\n",
    "    else:\n",
    "        tier_2_scenes.append(scene_id)\n",
    "        \n",
    "\n",
    "tier_1_scenes = set(tier_1_scenes)\n",
    "tier_2_scenes = set(tier_2_scenes)\n",
    "\n",
    "print('tier 1 scenes')\n",
    "print(tier_1_scenes)\n",
    "print('tier 2 scenes')\n",
    "print(tier_2_scenes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>img_uri</th>\n",
       "      <th>label_uri</th>\n",
       "      <th>area</th>\n",
       "      <th>tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>train_tier_1/znz/06f252/06f252.tif</td>\n",
       "      <td>train_tier_1/znz/06f252-labels/06f252.geojson</td>\n",
       "      <td>znz</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               img_uri  \\\n",
       "22  train_tier_1/znz/06f252/06f252.tif   \n",
       "\n",
       "                                        label_uri area  tier  \n",
       "22  train_tier_1/znz/06f252-labels/06f252.geojson  znz     1  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area = metadata['img_uri'].str.contains('06f252')\n",
    "metadata[area]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata = pd.read_csv('https://s3.amazonaws.com/drivendata/data/60/public/train_metadata.csv')\n",
    "areacounts = defaultdict(dict)\n",
    "for scene_id in counts:\n",
    "    area = metadata[metadata['img_uri'].str.contains(scene_id)]['area'].iloc[0]\n",
    "    if area in tier_2_scenes:\n",
    "        print(area, 'in tier 2, skipping')\n",
    "    try:\n",
    "        areacounts[area]['_total'] += counts[scene_id]\n",
    "    except:\n",
    "        areacounts[area]['_total'] = counts[scene_id]\n",
    "    \n",
    "    areacounts[area][scene_id] = counts[scene_id]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {'znz': {'_total': 19717,\n",
       "              '076995': 1701,\n",
       "              '75cdfa': 2304,\n",
       "              '425403': 1444,\n",
       "              '33cae6': 1106,\n",
       "              '06f252': 2387,\n",
       "              'e52478': 601,\n",
       "              'c7415c': 1346,\n",
       "              'bc32f1': 1521,\n",
       "              '3f8360': 1086,\n",
       "              'aee7fd': 1521,\n",
       "              '9b8638': 1482,\n",
       "              'bd5c14': 1369,\n",
       "              '3b20d4': 1849},\n",
       "             'dar': {'_total': 12356,\n",
       "              '353093': 1364,\n",
       "              'f883a0': 4094,\n",
       "              '0a4c40': 1604,\n",
       "              '42f235': 2257,\n",
       "              'a017f9': 1642,\n",
       "              'b15fce': 1395},\n",
       "             'acc': {'_total': 10603,\n",
       "              '665946': 6982,\n",
       "              'a42435': 1318,\n",
       "              'ca041a': 1566,\n",
       "              'd41d81': 737},\n",
       "             'ptn': {'_total': 44, 'abe1a3': 31, 'f49f31': 13},\n",
       "             'kam': {'_total': 868, '4e7c7f': 868},\n",
       "             'mon': {'_total': 803,\n",
       "              '401175': 192,\n",
       "              '493701': 280,\n",
       "              'f15272': 205,\n",
       "              '207cc7': 126},\n",
       "             'nia': {'_total': 65, '825a50': 65}})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "areacounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_tiles = sum(areacounts.values())\n",
    "area_pcts = {id: num/total_tiles for id, num in areacounts.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int,\n",
       "            {'znz': 19717,\n",
       "             'dar': 12356,\n",
       "             'acc': 10603,\n",
       "             'ptn': 44,\n",
       "             'kam': 868,\n",
       "             'mon': 803,\n",
       "             'nia': 65})"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "areacounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'znz': 0.4435171855317617,\n",
       " 'dar': 0.27793773618859097,\n",
       " 'acc': 0.23850548857297102,\n",
       " 'ptn': 0.0009897426669066042,\n",
       " 'kam': 0.01952492351988483,\n",
       " 'mon': 0.018062803671045527,\n",
       " 'nia': 0.0014621198488393018}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_pcts"
   ]
  }
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