【数据集转换】VOC数据集转COCO数据集·代码实现+操作步骤

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.pyimport os
import argparse
import json
import xml.etree.ElementTree as ET
from typing import Dict, List
import redef 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 listif 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 listext_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_pathsdef 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_infodef 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')) - 1ymin = int(bndbox.findtext('ymin')) - 1xmax = 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 - xmino_height = ymax - yminann = {'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 anndef 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 xmlann_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 + 1for 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.pyimport os
import shutilimages_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")