{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download Annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://images.cocodataset.org/annotations/'\n",
    "files = [\n",
    "    'annotations_trainval2017.zip'\n",
    "]\n",
    "for file in files:\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keypoint Detection Task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = COCO('annotations/person_keypoints_train2017.json')\n",
    "val_data = COCO('annotations/person_keypoints_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/keypoints_{split}.jsonl', mode='w') as writer:\n",
    "        for image_id, image_info in tqdm(data.imgs.items()):\n",
    "            bboxes, keypoints = [], []\n",
    "            anns = data.imgToAnns[image_id]\n",
    "            if len(anns) > 0:\n",
    "                \n",
    "                for ann in anns:\n",
    "                    bboxes.append(ann['bbox'])\n",
    "                    keypoints.append(ann['keypoints'])\n",
    "                writer.write({\n",
    "                    'image': image_info['file_name'],\n",
    "                    'bboxes': bboxes,\n",
    "                    'keypoints': np.array(keypoints).reshape(\n",
    "                        len(bboxes), -1, 3\n",
    "                    ).tolist()\n",
    "                })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for split in ['train', 'validation']:\n",
    "    file_path = f'data/keypoints_{split}.jsonl'\n",
    "    with zipfile.ZipFile(f'data/keypoints_{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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