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，具体看注释文章来源：https://www.toymoban.com/news/detail-444653.html
import sys
import os
import json
import xml.etree.ElementTree as ET
START_BOUNDING_BOX_ID = 0
PRE_DEFINE_CATEGORIES = {"air-hole": 1, "broken-arc": 2, "hollow-bead": 3,
"overlap": 4, "unfused": 5, "bite-edge": 6, "crack": 7,
"slag-inclusion": 8} # 修改的地方，修改为自己的类别
# If necessary, pre-define category and its id
# PRE_DEFINE_CATEGORIES = {"aeroplane": 1, "bicycle": 2, "bird": 3, "boat": 4,
# "bottle":5, "bus": 6, "car": 7, "cat": 8, "chair": 9,
# "cow": 10, "diningtable": 11, "dog": 12, "horse": 13,
# "motorbike": 14, "person": 15, "pottedplant": 16,
# "sheep": 17, "sofa": 18, "train": 19, "tvmonitor": 20}
def get(root, name):
vars = root.findall(name)
return vars
def get_and_check(root, name, length):
vars = root.findall(name)
if len(vars) == 0:
raise NotImplementedError('Can not find %s in %s.' % (name, root.tag))
if length > 0 and len(vars) != length:
raise NotImplementedError('The size of %s is supposed to be %d, but is %d.' % (name, length, len(vars)))
if length == 1:
vars = vars[0]
return vars
def get_filename_as_int(filename):
try:
filename = os.path.splitext(filename)[0]
return filename
except:
raise NotImplementedError('Filename %s is supposed to be an integer.' % (filename))
# xml_list为xml文件存放的txt文件名 xml_dir为真实xml的存放路径 json_file为存放的json路径
def convert(xml_list, xml_dir, json_file):
list_fp = open(xml_list, 'r')
json_dict = {"images": [], "type": "instances", "annotations": [],
"categories": []}
categories = PRE_DEFINE_CATEGORIES
bnd_id = START_BOUNDING_BOX_ID
for line in list_fp:
line = line.strip()
line = line + ".xml"
print("Processing %s" % (line))
xml_f = os.path.join(xml_dir, line)
tree = ET.parse(xml_f)
root = tree.getroot()
path = get(root, 'path')
if len(path) == 1:
filename = os.path.basename(path[0].text)
elif len(path) == 0:
filename = get_and_check(root, 'filename', 1).text
else:
raise NotImplementedError('%d paths found in %s' % (len(path), line))
## The filename must be a number
image_id = get_filename_as_int(filename)
size = get_and_check(root, 'size', 1)
width = int(get_and_check(size, 'width', 1).text)
height = int(get_and_check(size, 'height', 1).text)
image = {'file_name': filename, 'height': height, 'width': width,
'id': image_id}
json_dict['images'].append(image)
## Cruuently we do not support segmentation
# segmented = get_and_check(root, 'segmented', 1).text
# assert segmented == '0'
for obj in get(root, 'object'):
category = get_and_check(obj, 'name', 1).text
if category not in categories:
new_id = len(categories)
categories[category] = new_id
category_id = categories[category]
bndbox = get_and_check(obj, 'bndbox', 1)
xmin = int(get_and_check(bndbox, 'xmin', 1).text) - 1
ymin = int(get_and_check(bndbox, 'ymin', 1).text) - 1
xmax = int(get_and_check(bndbox, 'xmax', 1).text)
ymax = int(get_and_check(bndbox, 'ymax', 1).text)
assert (xmax > xmin)
assert (ymax > ymin)
o_width = abs(xmax - xmin)
o_height = abs(ymax - ymin)
ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id':
image_id, 'bbox': [xmin, ymin, o_width, o_height],
'category_id': category_id, 'id': bnd_id, 'ignore': 0,
'segmentation': []}
json_dict['annotations'].append(ann)
bnd_id = bnd_id + 1
for cate, cid in categories.items():
cat = {'supercategory': 'none', 'id': cid, 'name': cate}
json_dict['categories'].append(cat)
json_fp = open(json_file, 'w')
json_str = json.dumps(json_dict)
json_fp.write(json_str)
json_fp.close()
list_fp.close()
if __name__ == '__main__':
# xml_list为xml文件存放的txt文件名 xml_dir为真实xml的存放路径 json_file为存放的json路径
# xml_list = './data/VOCdevkit/ImageSets/Main/test.txt'
# xml_list = './data/VOCdevkit/ImageSets/Main/train.txt'
xml_list = './data/VOCdevkit/ImageSets/Main/val.txt'
xml_dir = './data/VOCdevkit/Annotations'
# json_dir = './data/COCO/annotations/test.json' # 注意！！！这里test.json先要自己创建，不然
# json_dir = './data/COCO/annotations/train.json' # 注意！！！这里test.json先要自己创建，不然
json_dir = './data/COCO/annotations/val.json' # 注意！！！这里test.json先要自己创建，不然 #程序回报权限不足
convert(xml_list, xml_dir, json_dir)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
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