{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import zipfile\n",
    "import requests\n",
    "import jsonlines\n",
    "from tqdm import tqdm\n",
    "from pathlib import Path\n",
    "from pycocotools.coco import COCO\n",
    "from pycocotools import mask as maskUtils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download Annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://images.cocodataset.org/annotations/'\n",
    "file = 'stuff_annotations_trainval2017.zip'\n",
    "if not Path(f'./{file}').exists():\n",
    "    response = requests.get(url + file)\n",
    "    with open(file, 'wb') as f:\n",
    "        f.write(response.content)\n",
    "\n",
    "    with zipfile.ZipFile(file, 'r') as zipf:\n",
    "        zipf.extractall(Path())\n",
    "\n",
    "# for split in ['train', 'val']:\n",
    "#     file = f'./annotations/stuff_{split}2017_pixelmaps'\n",
    "#     if not Path(file).exists():\n",
    "#         with zipfile.ZipFile(file + '.zip', 'r') as zipf:\n",
    "#             zipf.extractall(Path('./annotations'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stuff Segmentation Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = COCO('annotations/stuff_train2017.json')\n",
    "val_data = COCO('annotations/stuff_val2017.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for split, data in zip(['train', 'validation'], [train_data, val_data]):\n",
    "    with jsonlines.open(f'data/stuff_{split}.jsonl', mode='w') as writer:\n",
    "        for image_id, image_info in tqdm(data.imgs.items()):\n",
    "            categories, sem_rles = [], []\n",
    "            anns = data.imgToAnns[image_id]\n",
    "            file_name = image_info['file_name']\n",
    "            height, width = image_info['height'], image_info['width']\n",
    "            for ann in anns:\n",
    "                categories.append(ann['category_id'] - 92)\n",
    "                segm = ann['segmentation']\n",
    "                if isinstance(segm, list):\n",
    "                    rles = maskUtils.frPyObjects(segm, height, width)\n",
    "                    rle = maskUtils.merge(rles)\n",
    "                    rle['counts'] = rle['counts'].decode()\n",
    "                elif isinstance(segm['counts'], list):\n",
    "                    rle = maskUtils.frPyObjects(segm, height, width)\n",
    "                else:\n",
    "                    rle = segm\n",
    "                sem_rles.append(rle)\n",
    "            writer.write({\n",
    "                'image': file_name, 'categories': categories, 'sem.rles': sem_rles\n",
    "            })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for split in ['train', 'validation']:\n",
    "    file_path = f'data/stuff_{split}.jsonl'\n",
    "    with zipfile.ZipFile(f'data/stuff_{split}.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:\n",
    "        zipf.write(file_path, os.path.basename(file_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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