Datasets:
Tasks:
Object Detection
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
License:
File size: 2,007 Bytes
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#!/usr/bin/env python3
from pycocotools.coco import COCO
import os
import shutil
import json
# MODIFY ME!
# ---------------------------------
coco_dataset='/opt/datasets/coco/'
year='2017'
splits=['train', 'val']
dst_dir='data'
metadata='metadata.jsonl'
# ---------------------------------
def process_split(coco_dataset, year, split, dst_dir, metadata):
annotations=f'{coco_dataset}/annotations/instances_{split}{year}.json'
coco=COCO(annotations)
cats =coco.loadCats(coco.getCatIds())
target_name='cell phone'
target_id=coco.getCatIds(catNms=[target_name])[0];
print(f'Found "{target_name}" id: {target_id}')
image_ids=coco.getImgIds(catIds=[target_id]);
images=coco.loadImgs(image_ids)
print(f'Found {len(images)} images containing a "{target_name}"')
image_dir=f'{coco_dataset}/{split}{year}/'
image_dst_dir=f'{dst_dir}/{split}/'
os.makedirs(image_dst_dir, exist_ok=True)
dst_metadata=f'{image_dst_dir}/{metadata}'
with open(dst_metadata, 'w') as f:
for image in images:
ann_ids = coco.getAnnIds(imgIds=image['id'], catIds=target_id, iscrowd=None)
anns = coco.loadAnns(ann_ids)
src_image=f'{image_dir}/{image["file_name"]}'
shutil.copy(src_image, image_dst_dir)
entry={}
entry['file_name']=image["file_name"]
entry['image_id']=image["id"]
boxes=[ann['bbox'] for ann in anns]
cats=[0]*len(boxes)
ids=[ann['id'] for ann in anns]
areas=[ann['area'] for ann in anns]
entry['objects']={
'bbox': boxes,
'category': cats,
'id': ids,
'area': areas
}
f.write(f'{json.dumps(entry)}\n')
print(f'Done processing the "{split}" split!')
if __name__ == "__main__":
for split in splits:
process_split(coco_dataset, year, split, dst_dir, metadata)
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