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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Download Annotations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"url = 'http://images.cocodataset.org/annotations/'\n",
"file = '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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Read annotations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"coco91_to_coco80 = [\n",
" None, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, None,\n",
" 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n",
" 23, None, 24, 25, None, None, 26, 27, 28, 29, 30,\n",
" 31, 32, 33, 34, 35, 36, 37, 38, 39, None, 40, 41,\n",
" 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,\n",
" 55, 56, 57, 58, 59, None, 60, None, None, 61, None,\n",
" 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, None,\n",
" 73, 74, 75, 76, 77, 78, 79\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Image Captioning Task"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"train_data = COCO('annotations/captions_train2017.json')\n",
"val_data = COCO('annotations/captions_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/captions_{split}.jsonl', mode='w') as writer:\n",
" for image_id, image_info in tqdm(data.imgs.items()):\n",
" captions = []\n",
" anns = data.imgToAnns[image_id]\n",
" for ann in anns:\n",
" captions.append(ann['caption'])\n",
" writer.write({\n",
" 'image': image_info['file_name'], 'captions': captions\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"for split in ['train', 'validation']:\n",
" file_path = f'data/captions_{split}.jsonl'\n",
" with zipfile.ZipFile(f'data/captions_{split}.zip', 'w', zipfile.ZIP_DEFLATED) as zipf:\n",
" zipf.write(file_path, os.path.basename(file_path))"
]
}
],
"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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