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Convert COCO to VOC
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import json | |
import os | |
from tqdm import tqdm | |
from xmltodict import unparse | |
# BBOX_OFFSET: Switch between 0-based and 1-based bbox. | |
# The COCO dataset is in 0-based format, while the VOC dataset is 1-based. | |
# To keep 0-based, set it to 0. To convert to 1-based, set it to 1. | |
BBOX_OFFSET = 0 | |
src_base = os.path.join("data", "coco", "annotations") | |
dst_base = os.path.join("data", "VOCdevkitCOCO", "VOCCOCO") | |
dst_dirs = {x: os.path.join(dst_base, x) for x in ["Annotations", "ImageSets", "JPEGImages"]} | |
dst_dirs['ImageSets'] = os.path.join(dst_dirs['ImageSets'], "Main") | |
for k, d in dst_dirs.items(): | |
os.makedirs(d, exist_ok=True) | |
def base_dict(filename, width, height, depth=3): | |
return { | |
"annotation": { | |
"filename": os.path.split(filename)[-1], | |
"folder": "VOCCOCO", "segmented": "0", "owner": {"name": "unknown"}, | |
"source": {'database': "The COCO 2017 database", 'annotation': "COCO 2017", "image": "unknown"}, | |
"size": {'width': width, 'height': height, "depth": depth}, | |
"object": [] | |
} | |
} | |
def base_object(size_info, name, bbox): | |
x1, y1, w, h = bbox | |
x2, y2 = x1 + w, y1 + h | |
width = size_info['width'] | |
height = size_info['height'] | |
x1 = max(x1, 0) + BBOX_OFFSET | |
y1 = max(y1, 0) + BBOX_OFFSET | |
x2 = min(x2, width - 1) + BBOX_OFFSET | |
y2 = min(y2, height - 1) + BBOX_OFFSET | |
return { | |
'name': name, 'pose': 'Unspecified', 'truncated': '0', 'difficult': '0', | |
'bndbox': {'xmin': x1, 'ymin': y1, 'xmax': x2, 'ymax': y2} | |
} | |
sets = { | |
"trainval": os.path.join(src_base, "instances_train2017.json"), | |
"test": os.path.join(src_base, "instances_val2017.json"), | |
} | |
cate = {x['id']: x['name'] for x in json.load(open(sets["test"]))['categories']} | |
for stage, filename in sets.items(): | |
print("Parse", filename) | |
data = json.load(open(filename)) | |
images = {} | |
for im in tqdm(data["images"], "Parse Images"): | |
img = base_dict(im['coco_url'], im['width'], im['height'], 3) | |
images[im["id"]] = img | |
for an in tqdm(data["annotations"], "Parse Annotations"): | |
ann = base_object(images[an['image_id']]['annotation']["size"], cate[an['category_id']], an['bbox']) | |
images[an['image_id']]['annotation']['object'].append(ann) | |
for k, im in tqdm(images.items(), "Write Annotations"): | |
im['annotation']['object'] = im['annotation']['object'] or [None] | |
unparse(im, | |
open(os.path.join(dst_dirs["Annotations"], "{}.xml".format(str(k).zfill(12))), "w"), | |
full_document=False, pretty=True) | |
print("Write image sets") | |
with open(os.path.join(dst_dirs["ImageSets"], "{}.txt".format(stage)), "w") as f: | |
f.writelines(list(map(lambda x: str(x).zfill(12) + "\n", images.keys()))) | |
print("OK") |
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