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{
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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