在自己的数据集上实验时，往往需要将VOC数据集转化为coco数据集，因为这种需求所以才记录这篇文章，代码出处未知，感谢开源。
在远程服务器上测试目标检测算法需要用到测试集，最常用的是coco2014/2017和voc07/12数据集。
coco数据集的地址为http://cocodataset.org/#download
voc和coco的镜像为https://pjreddie.com/projects/pascal-voc-dataset-mirror/
一、数据集格式对比
1.1 VOC数据集
VOC_ROOT #根目录
├── JPEGImages # 存放源图，(当然图片并不一定要是**.jpg格式的，只是规定文件夹名字叫JPEGImages**)；
│ ├── aaaa.jpg
│ ├── bbbb.jpg
│ └── cccc.jpg
├── Annotations # 存放xml文件，VOC的标注是xml格式，与JPEGImages中的图片一一对应
│ ├── aaaa.xml
│ ├── bbbb.xml
│ └── cccc.xml
└── ImageSets
└── Main
├── train.txt # txt文件中每一行包含一个图片的名称
└── val.txt
1.2 COCO数据集
COCO_ROOT #根目录
├── annotations # 存放json格式的标注
│ ├── instances_train2017.json
│ └── instances_val2017.json
└── train2017 # 存放图片文件
│ ├── 000000000001.jpg
│ ├── 000000000002.jpg
│ └── 000000000003.jpg
└── val2017
├── 000000000004.jpg
└── 000000000005.jpg
1.2.3 json标注格式
与VOC一个文件一个xml标注不同，COCO所有的目标框标注都是放在一个json文件中的。
这个json文件解析出来是一个字典，格式如下：
{
"info": info,
"images": [image],
"annotations": [annotation],
"categories": [categories],
"licenses": [license],
}
二、转换步骤
2.1 程序总体目录
2.2 标签文件转换代码实现（xml文件转json格式）VOC_To_CoCo_01.py
这里需要运行三次，因为train.txt val.txt test.txt是三个文件，具体看注释
# VOC_To_CoCo_01.py
import os
import argparse
import json
import xml.etree.ElementTree as ET
from typing import Dict, List
import re
def get_label2id(labels_path: str) -> Dict[str, int]:
"""id is 1 start"""
with open(labels_path, 'r') as f:
labels_str = f.read().split()
labels_ids = list(range(1, len(labels_str) + 1))
return dict(zip(labels_str, labels_ids))
def get_annpaths(ann_dir_path: str = None,
ann_ids_path: str = None,
ext: str = '',
annpaths_list_path: str = None) -> List[str]:
# If use annotation paths list
if annpaths_list_path is not None:
with open(annpaths_list_path, 'r') as f:
ann_paths = f.read().split()
return ann_paths
# If use annotaion ids list
ext_with_dot = '.' + ext if ext != '' else ''
with open(ann_ids_path, 'r') as f:
ann_ids = f.read().split()
ann_paths = [os.path.join(ann_dir_path, aid + ext_with_dot) for aid in ann_ids]
return ann_paths
def get_image_info(annotation_root, extract_num_from_imgid=True):
path = annotation_root.findtext('path')
if path is None:
filename = annotation_root.findtext('filename')
else:
filename = os.path.basename(path)
img_name = os.path.basename(filename)
img_id = os.path.splitext(img_name)[0]
if extract_num_from_imgid and isinstance(img_id, str):
img_id = int(re.findall(r'\d+', img_id)[0])
size = annotation_root.find('size')
width = int(size.findtext('width'))
height = int(size.findtext('height'))
image_info = {
'file_name': filename,
'height': height,
'width': width,
'id': img_id
}
return image_info
def get_coco_annotation_from_obj(obj, label2id):
label = obj.findtext('name')
assert label in label2id, f"Error: {label} is not in label2id !"
category_id = label2id[label]
bndbox = obj.find('bndbox')
xmin = int(bndbox.findtext('xmin')) - 1
ymin = int(bndbox.findtext('ymin')) - 1
xmax = int(bndbox.findtext('xmax'))
ymax = int(bndbox.findtext('ymax'))
assert xmax > xmin and ymax > ymin, f"Box size error !: (xmin, ymin, xmax, ymax): {xmin, ymin, xmax, ymax}"
o_width = xmax - xmin
o_height = ymax - ymin
ann = {
'area': o_width * o_height,
'iscrowd': 0,
'bbox': [xmin, ymin, o_width, o_height],
'category_id': category_id,
'ignore': 0,
'segmentation': [] # This script is not for segmentation
}
return ann
def convert_xmls_to_cocojson(annotation_paths: List[str],
label2id: Dict[str, int],
output_jsonpath: str,
extract_num_from_imgid: bool = True):
output_json_dict = {
"images": [],
"type": "instances",
"annotations": [],
"categories": []
}
bnd_id = 1 # START_BOUNDING_BOX_ID, TODO input as args ?
for a_path in annotation_paths:
# Read annotation xml
ann_tree = ET.parse(a_path)
ann_root = ann_tree.getroot()
img_info = get_image_info(annotation_root=ann_root,
extract_num_from_imgid=extract_num_from_imgid)
img_id = img_info['id']
output_json_dict['images'].append(img_info)
for obj in ann_root.findall('object'):
ann = get_coco_annotation_from_obj(obj=obj, label2id=label2id)
ann.update({'image_id': img_id, 'id': bnd_id})
output_json_dict['annotations'].append(ann)
bnd_id = bnd_id + 1
for label, label_id in label2id.items():
category_info = {'supercategory': 'none', 'id': label_id, 'name': label}
output_json_dict['categories'].append(category_info)
with open(output_jsonpath, 'w') as f:
output_json = json.dumps(output_json_dict)
f.write(output_json)
print('Convert successfully !')
def main():
parser = argparse.ArgumentParser(
description='This script support converting voc format xmls to coco format json')
parser.add_argument('--ann_dir', type=str, default='./VOCdevkit/Annotations')
parser.add_argument('--ann_ids', type=str, default='./VOCdevkit/ImageSets/Main/val.txt') # 这里修改 train val test 一共修改三次
#parser.add_argument('--ann_ids', type=str, default='./VOCdevkit/ImageSets/Main/train.txt')
#parser.add_argument('--ann_ids', type=str, default='./VOCdevkit/ImageSets/Main/test.txt')
parser.add_argument('--ann_paths_list', type=str, default=None)
parser.add_argument('--labels', type=str, default='./VOCdevkit/labels.txt')
parser.add_argument('--output', type=str, default='./output/annotations/val.json') # 这里修改 train val test 一共修改三次
#parser.add_argument('--output', type=str, default='./output/annotations/train.json')
#parser.add_argument('--output', type=str, default='./output/annotations/test.json')
parser.add_argument('--ext', type=str, default='xml')
args = parser.parse_args()
label2id = get_label2id(labels_path=args.labels)
ann_paths = get_annpaths(
ann_dir_path=args.ann_dir,
ann_ids_path=args.ann_ids,
ext=args.ext,
annpaths_list_path=args.ann_paths_list
)
convert_xmls_to_cocojson(
annotation_paths=ann_paths,
label2id=label2id,
output_jsonpath=args.output,
extract_num_from_imgid=True
)
if __name__ == '__main__':
if not os.path.exists('./output/annotations'):
os.makedirs('./output/annotations')
main()2.3 数据集图像文件copy代码实现（复制图片数据集到coco中）VOC_To_CoCo_02.py
# VOC_To_CoCo_02.py
import os
import shutil
images_file_path = './VOCdevkit/JPEGImages/'
split_data_file_path = './VOCdevkit/ImageSets/Main/'
new_images_file_path = './output/'
if not os.path.exists(new_images_file_path + 'train'):
os.makedirs(new_images_file_path + 'train')
if not os.path.exists(new_images_file_path + 'val'):
os.makedirs(new_images_file_path + 'val')
if not os.path.exists(new_images_file_path + 'test'):
os.makedirs(new_images_file_path + 'test')
dst_train_Image = new_images_file_path + 'train/'
dst_val_Image = new_images_file_path + 'val/'
dst_test_Image = new_images_file_path + 'test/'
total_txt = os.listdir(split_data_file_path)
for i in total_txt:
name = i[:-4]
if name == 'train':
txt_file = open(split_data_file_path + i, 'r')
for line in txt_file:
line = line.strip('\n')
line = line.strip('\r')
srcImage = images_file_path + line + '.jpg'
dstImage = dst_train_Image + line + '.jpg'
shutil.copyfile(srcImage, dstImage)
txt_file.close()
elif name == 'val':
txt_file = open(split_data_file_path + i, 'r')
for line in txt_file:
line = line.strip('\n')
line = line.strip('\r')
srcImage = images_file_path + line + '.jpg'
dstImage = dst_val_Image + line + '.jpg'
shutil.copyfile(srcImage, dstImage)
txt_file.close()
elif name == 'test':
txt_file = open(split_data_file_path + i, 'r')
for line in txt_file:
line = line.strip('\n')
line = line.strip('\r')
srcImage = images_file_path + line + '.jpg'
dstImage = dst_test_Image + line + '.jpg'
shutil.copyfile(srcImage, dstImage)
txt_file.close()
else:
print("Error, Please check the file name of folder")三、效果展示
