xview-coco-explained.ipynb

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 "cells": [
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   "id": "a6507a21-024c-48b1-a159-1724fe8cd052",
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     "text": [
      "/vol/bitbucket/jrb21/anaconda3/envs/sodb/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "from collections import defaultdict\n",
    "from pathlib import Path\n",
    "from typing import Dict, List\n",
    "\n",
    "import fire\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from sahi.utils.coco import Coco, CocoAnnotation, CocoCategory, CocoImage\n",
    "from sahi.utils.file import load_json, save_json\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e345433-c979-49a8-acf6-d7757b70aa13",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images_dir = \"/vol/bitbucket/jrb21/project/xView/data/train/images\"\n",
    "train_geojson_path = \"/vol/bitbucket/jrb21/project/xView/data/xView_train.geojson\"\n",
    "output_dir = \"/vol/bitbucket/jrb21/project/xView/data/coco\"\n",
    "train_split_rate=0.75\n",
    "category_id_remapping=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd79797e-5145-4e7d-951d-b18a1a36a185",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'11': 'Fixed-wing Aircraft',\n",
       " '12': 'Small Aircraft',\n",
       " '13': 'Cargo Plane',\n",
       " '15': 'Helicopter',\n",
       " '17': 'Passenger Vehicle',\n",
       " '18': 'Small Car',\n",
       " '19': 'Bus',\n",
       " '20': 'Pickup Truck',\n",
       " '21': 'Utility Truck',\n",
       " '23': 'Truck',\n",
       " '24': 'Cargo Truck',\n",
       " '25': 'Truck w/Box',\n",
       " '26': 'Truck Tractor',\n",
       " '27': 'Trailer',\n",
       " '28': 'Truck w/Flatbed',\n",
       " '29': 'Truck w/Liquid',\n",
       " '32': 'Crane Truck',\n",
       " '33': 'Railway Vehicle',\n",
       " '34': 'Passenger Car',\n",
       " '35': 'Cargo Car',\n",
       " '36': 'Flat Car',\n",
       " '37': 'Tank car',\n",
       " '38': 'Locomotive',\n",
       " '40': 'Maritime Vessel',\n",
       " '41': 'Motorboat',\n",
       " '42': 'Sailboat',\n",
       " '44': 'Tugboat',\n",
       " '45': 'Barge',\n",
       " '47': 'Fishing Vessel',\n",
       " '49': 'Ferry',\n",
       " '50': 'Yacht',\n",
       " '51': 'Container Ship',\n",
       " '52': 'Oil Tanker',\n",
       " '53': 'Engineering Vehicle',\n",
       " '54': 'Tower crane',\n",
       " '55': 'Container Crane',\n",
       " '56': 'Reach Stacker',\n",
       " '57': 'Straddle Carrier',\n",
       " '59': 'Mobile Crane',\n",
       " '60': 'Dump Truck',\n",
       " '61': 'Haul Truck',\n",
       " '62': 'Scraper/Tractor',\n",
       " '63': 'Front loader/Bulldozer',\n",
       " '64': 'Excavator',\n",
       " '65': 'Cement Mixer',\n",
       " '66': 'Ground Grader',\n",
       " '71': 'Hut/Tent',\n",
       " '72': 'Shed',\n",
       " '73': 'Building',\n",
       " '74': 'Aircraft Hangar',\n",
       " '76': 'Damaged Building',\n",
       " '77': 'Facility',\n",
       " '79': 'Construction Site',\n",
       " '83': 'Vehicle Lot',\n",
       " '84': 'Helipad',\n",
       " '86': 'Storage Tank',\n",
       " '89': 'Shipping container lot',\n",
       " '91': 'Shipping Container',\n",
       " '93': 'Pylon',\n",
       " '94': 'Tower'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# init vars\n",
    "category_id_to_name = {}\n",
    "with open(\"xview/xview_class_labels.txt\", encoding=\"utf8\") as f:\n",
    "    lines = f.readlines()\n",
    "for line in lines:\n",
    "    category_id = line.split(\":\")[0]\n",
    "    category_name = line.split(\":\")[1].replace(\"\\n\", \"\")\n",
    "    category_id_to_name[category_id] = category_name\n",
    "    \n",
    "    \n",
    "category_id_to_name"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "{'11': '0',\n",
       " '12': '1',\n",
       " '13': '2',\n",
       " '15': '3',\n",
       " '17': '4',\n",
       " '18': '5',\n",
       " '19': '6',\n",
       " '20': '7',\n",
       " '21': '8',\n",
       " '23': '9',\n",
       " '24': '10',\n",
       " '25': '11',\n",
       " '26': '12',\n",
       " '27': '13',\n",
       " '28': '14',\n",
       " '29': '15',\n",
       " '32': '16',\n",
       " '33': '17',\n",
       " '34': '18',\n",
       " '35': '19',\n",
       " '36': '20',\n",
       " '37': '21',\n",
       " '38': '22',\n",
       " '40': '23',\n",
       " '41': '24',\n",
       " '42': '25',\n",
       " '44': '26',\n",
       " '45': '27',\n",
       " '47': '28',\n",
       " '49': '29',\n",
       " '50': '30',\n",
       " '51': '31',\n",
       " '52': '32',\n",
       " '53': '33',\n",
       " '54': '34',\n",
       " '55': '35',\n",
       " '56': '36',\n",
       " '57': '37',\n",
       " '59': '38',\n",
       " '60': '39',\n",
       " '61': '40',\n",
       " '62': '41',\n",
       " '63': '42',\n",
       " '64': '43',\n",
       " '65': '44',\n",
       " '66': '45',\n",
       " '71': '46',\n",
       " '72': '47',\n",
       " '73': '48',\n",
       " '74': '49',\n",
       " '76': '50',\n",
       " '77': '51',\n",
       " '79': '52',\n",
       " '83': '53',\n",
       " '84': '54',\n",
       " '86': '55',\n",
       " '89': '56',\n",
       " '91': '57',\n",
       " '93': '58',\n",
       " '94': '59'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "if category_id_remapping is None:\n",
    "    category_id_remapping = load_json(\"xview/category_id_mapping.json\")\n",
    "category_id_remapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b39fbd13-c9fe-4408-815d-4871a6c39c2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# init coco object\n",
    "coco = Coco()\n",
    "# append categories\n",
    "for category_id, category_name in category_id_to_name.items():\n",
    "    if category_id in category_id_remapping.keys():\n",
    "        remapped_category_id = category_id_remapping[category_id]\n",
    "        coco.add_category(\n",
    "            CocoCategory(id=int(remapped_category_id), name=category_name)\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fa3f241d-763a-4add-a8e4-ffae1b2c6440",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_labels(fname):\n",
    "    \"\"\"\n",
    "    Gets label data from a geojson label file\n",
    "    Args:\n",
    "        fname: file path to an xView geojson label file\n",
    "    Output:\n",
    "        Returns three arrays: coords, chips, and classes corresponding to the\n",
    "            coordinates, file-names, and classes for each ground truth.\n",
    "    Modified from https://github.com/DIUx-xView.\n",
    "    \"\"\"\n",
    "    data = load_json(fname)\n",
    "\n",
    "    coords = np.zeros((len(data[\"features\"]), 4))\n",
    "    chips = np.zeros((len(data[\"features\"])), dtype=\"object\")\n",
    "    classes = np.zeros((len(data[\"features\"])))\n",
    "    image_name_to_annotation_ind = defaultdict(list)\n",
    "\n",
    "    for i in tqdm(range(len(data[\"features\"])), \"Parsing xView data\"):\n",
    "        if data[\"features\"][i][\"properties\"][\"bounds_imcoords\"] != []:\n",
    "            b_id = data[\"features\"][i][\"properties\"][\"image_id\"]\n",
    "            # https://github.com/DIUx-xView/xView1_baseline/issues/3\n",
    "            if b_id == \"1395.tif\":\n",
    "                continue\n",
    "            val = np.array(\n",
    "                [\n",
    "                    int(num)\n",
    "                    for num in data[\"features\"][i][\"properties\"][\n",
    "                        \"bounds_imcoords\"\n",
    "                    ].split(\",\")\n",
    "                ]\n",
    "            )\n",
    "            chips[i] = b_id\n",
    "            classes[i] = data[\"features\"][i][\"properties\"][\"type_id\"]\n",
    "\n",
    "            image_name_to_annotation_ind[b_id].append(i)\n",
    "\n",
    "            if val.shape[0] != 4:\n",
    "                print(\"Issues at %d!\" % i)\n",
    "            else:\n",
    "                coords[i] = val\n",
    "        else:\n",
    "            chips[i] = \"None\"\n",
    "\n",
    "    return coords, chips, classes, image_name_to_annotation_ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8b32d1ed-e19f-4a72-ad6b-2d2f87bd1da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsing xView data: 100%|███████████████████████████████████████████████████| 601937/601937 [00:01<00:00, 334990.31it/s]\n"
     ]
    }
   ],
   "source": [
    "    # parse xview data\n",
    "    coords, chips, classes, image_name_to_annotation_ind = get_labels(\n",
    "        train_geojson_path)"
   ]
  },
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   "cell_type": "code",
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     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_name_to_annotation_ind[\"2355.tif\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c91cfe64-91ac-4184-8a48-865044734071",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
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'2341.tif', '2353.tif', '2354.tif', '492.tif', '494.tif', '509.tif', '513.tif', '531.tif', '535.tif', '2114.tif', '2115.tif', '2124.tif', '2125.tif', '2131.tif', '2139.tif', '2150.tif', '1178.tif', '1180.tif', '1182.tif', '1184.tif', '1185.tif', '1193.tif', '1196.tif', '1197.tif', '1224.tif', '1233.tif', '1237.tif', '1253.tif', '1255.tif', '1256.tif', '2359.tif', '2361.tif', '2365.tif', '2373.tif', '2375.tif', '2389.tif', '2391.tif', '2405.tif', '2413.tif', '2414.tif', '2416.tif', '2425.tif', '2436.tif', '2438.tif', '2440.tif', '927.tif', '983.tif', '1058.tif', '1496.tif', '1505.tif', '1507.tif', '1508.tif', '1509.tif', '1511.tif', '1523.tif', '1529.tif', '1530.tif', '680.tif', '682.tif', '111.tif', '112.tif', '129.tif', '130.tif', '131.tif', '145.tif', '157.tif', '158.tif', '1630.tif', '1633.tif', '1653.tif', '1654.tif', '1656.tif', '1673.tif', '1675.tif', '1677.tif', '1679.tif', '1694.tif', '1695.tif', '1904.tif', '1906.tif', '1918.tif', '1920.tif', '1926.tif', '1928.tif', '1930.tif', '1931.tif', '1938.tif', '1940.tif', '2055.tif', '1863.tif', '1870.tif', '1875.tif', '1880.tif', '1881.tif', '1883.tif', '1886.tif', '1887.tif', '1888.tif', '1889.tif', '1891.tif', '1892.tif', '1893.tif', '1897.tif', '1898.tif', '1899.tif', '1900.tif', '1902.tif', '1908.tif', '1910.tif', '1912.tif', '1913.tif', '1914.tif', '33.tif', '40.tif', '41.tif', '1149.tif', '1150.tif', '1155.tif', '1158.tif', '2382.tif', '2400.tif', '2462.tif', '2484.tif', '2486.tif', '2504.tif', '2516.tif', '2518.tif', '2552.tif', '2561.tif', '2565.tif', '2567.tif', '2568.tif', '2571.tif', '2573.tif', '2574.tif', '2584.tif', '2619.tif', '107.tif', '109.tif', '110.tif', '124.tif', '126.tif', '128.tif', '144.tif', '149.tif', '159.tif', '163.tif', '481.tif', '331.tif', '333.tif', '340.tif', '342.tif', '345.tif', '370.tif', '371.tif', '386.tif', '389.tif'])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_name_to_annotation_ind.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "69ddc68c-93f2-4557-8a7c-aff95f44ae7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ordered_image_name_list(image_name_to_annotation_ind: Dict):\n",
    "    image_name_list: List[str] = list(image_name_to_annotation_ind.keys())\n",
    "\n",
    "    def get_image_ind(image_name: str):\n",
    "        return int(image_name.split(\".\")[0])\n",
    "\n",
    "    image_name_list.sort(key=get_image_ind)\n",
    "\n",
    "    return image_name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "64619db9-012f-4fa5-ba3b-7a4cc4d57802",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_name_list = get_ordered_image_name_list(image_name_to_annotation_ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4f7da856-6d3d-4ae4-9361-bbdbf190825c",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
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       " '2547.tif',\n",
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       " '2552.tif',\n",
       " '2555.tif',\n",
       " '2557.tif',\n",
       " '2559.tif',\n",
       " '2560.tif',\n",
       " '2561.tif',\n",
       " '2562.tif',\n",
       " '2564.tif',\n",
       " '2565.tif',\n",
       " '2567.tif',\n",
       " '2568.tif',\n",
       " '2571.tif',\n",
       " '2573.tif',\n",
       " '2574.tif',\n",
       " '2584.tif',\n",
       " '2587.tif',\n",
       " '2589.tif',\n",
       " '2591.tif',\n",
       " '2596.tif',\n",
       " '2599.tif',\n",
       " '2606.tif',\n",
       " '2608.tif',\n",
       " '2609.tif',\n",
       " '2618.tif',\n",
       " '2619.tif']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_name_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99fccf88-7d8c-4081-9a23-d119937d2abe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert xView data to COCO format\n",
    "for image_name in tqdm(image_name_list, \"Converting xView data into COCO format\"):\n",
    "    # create coco image object\n",
    "    width, height = Image.open(Path(train_images_dir) / image_name).size\n",
    "    coco_image = CocoImage(file_name=image_name, height=height, width=width)\n",
    "\n",
    "    annotation_ind_list = image_name_to_annotation_ind[image_name]\n",
    "\n",
    "    # iterate over image annotations\n",
    "    for annotation_ind in annotation_ind_list:\n",
    "        bbox = coords[annotation_ind].tolist()\n",
    "        category_id = str(int(classes[annotation_ind].item()))\n",
    "        coco_bbox = [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]]\n",
    "        if category_id in category_id_remapping.keys():\n",
    "            category_name = category_id_to_name[category_id]\n",
    "            remapped_category_id = category_id_remapping[category_id]\n",
    "        else:\n",
    "            continue\n",
    "        # create coco annotation and append it to coco image\n",
    "        coco_annotation = CocoAnnotation(\n",
    "            bbox=coco_bbox,\n",
    "            category_id=int(remapped_category_id),\n",
    "            category_name=category_name,\n",
    "        )\n",
    "        if coco_annotation.area > 0:\n",
    "            coco_image.add_annotation(coco_annotation)\n",
    "    coco.add_image(coco_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "51301d20-94bb-4b20-8232-6f98cf5c1958",
   "metadata": {},
   "outputs": [],
   "source": [
    "category_id_remapping"
   ]
  }
 ],
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