以下脚本可以根据创建VOC格式数据集转换为COCO数据集
其中文件组织格式如下
VOC2007
------Annotations
------***********.xml
------***********.xml
-------ImageSets
------train.txt
------test.txt
-------JPEGImages
------***********.jpg
------***********.jpg
COCO2017
------train
------annotations
------train.json
------images
------***********.jpg
------***********.jpg
-------test
------annotations
------train.json
------images
------***********.jpg
------***********.jpg
#!/usr/bin/python
# xml是voc的格式
# json是coco的格式
import sys, os, json, glob
import xml.etree.ElementTree as ET
import shutil
INITIAL_BBOXIds = 1
# PREDEF_CLASSE = {}
PREDEF_CLASSE = {'DT_SUBSIDENCE':0, 'PIP':1, 'NET':2, 'GROUND_LAMP':3, 'DT_CRACK':4, 'PIP_S':5, 'DT_GAP':6}
# function
def get(root, name):
return root.findall(name)
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 convert(xml_paths, out_json, mode = "train"):
json_dict = {'images': [], 'type': 'instances',
'categories': [], 'annotations': []}
categories = PREDEF_CLASSE
bbox_id = INITIAL_BBOXIds
for image_id, xml_f in enumerate(xml_paths):
# 进度输出
# sys.stdout.write('\r>> Converting image %d/%d' % (
# image_id + 1, len(xml_paths)))
# sys.stdout.flush()
tree = ET.parse(xml_f)
root = tree.getroot()
# filename = get_and_check(root, 'frame', 1).text
# print(type(xml_f))
filename = xml_f.split(r"E:\VOC2007\Annotations")[1] # VOC的annotations的路径
filename = filename.split("\\")[1]
filename = filename.split(".xml")[0]+".jpg"
print("########################",filename,xml_f)
if mode == "test":
src = os.path.join(r"E:\VOC2007\JPEGImages",filename) # VOC的JPEGImages的路径
dst = os.path.join(r"E:\COCO2017\test\images",filename) # 需要存放的COCOimage路径
shutil.copyfile(src, dst)
if mode == "train":
src = os.path.join(r"E:\VOC2007\JPEGImages",filename)
dst = os.path.join(r"E:COCO2017\train\images",filename)
shutil.copyfile(src, dst)
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 + 1}
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 = max(categories.values()) + 1
categories[category] = new_id
category_id = categories[category]
bbox = get_and_check(obj, 'bndbox', 1)
xmin = int(get_and_check(bbox, 'xmin', 1).text) - 1
ymin = int(get_and_check(bbox, 'ymin', 1).text) - 1
xmax = int(get_and_check(bbox, 'xmax', 1).text)
ymax = int(get_and_check(bbox, 'ymax', 1).text)
if xmax <= xmin or ymax <= ymin:
continue
o_width = abs(xmax - xmin)
o_height = abs(ymax - ymin)
ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id': image_id + 1,
'bbox': [xmin, ymin, o_width, o_height], 'category_id': category_id,
'id': bbox_id, 'ignore': 0, 'segmentation': []}
json_dict['annotations'].append(ann)
bbox_id = bbox_id + 1
for cate, cid in categories.items():
cat = {'supercategory': 'none', 'id': cid, 'name': cate}
json_dict['categories'].append(cat)
# json_file = open(out_json, 'w')
# json_str = json.dumps(json_dict)
# json_file.write(json_str)
# json_file.close() # 快
json.dump(json_dict, open(out_json, 'w'), indent=4) # indent=4 更加美观显示 慢
if __name__ == '__main__':
# 1、读取VOC2007\ImagesSets\Main中的文件 得到测试集和训练集的图片名
test_filenames = []
train_filenames = []
with open(r"E:\lijunjie\GPR\radar_data\AUD_VOC\VOC2007\ImageSets\Main\test.txt") as f:
for filename in f.readlines():
test_filenames.append(filename.split('\n')[0]) # 去掉换行符 \n
with open(r"E:\lijunjie\GPR\radar_data\AUD_VOC\VOC2007\ImageSets\Main\train.txt") as f:
for filename in f.readlines():
train_filenames.append(filename.split('\n')[0])
print(test_filenames)
print(train_filenames)
# 2、分别拼凑出 训练 和 测试 所需要的xml 路径
xml_path = r'E:\VOC2007\Annotations' # 改一下读取xml文件位置
train_xml_files = []
test_xml_files = []
for train_filename in train_filenames:
train_xml_file = os.path.join(xml_path,train_filename) + '.xml' # 拼接路径 加上后缀
# print(train_xml_file)
train_xml_files.append(train_xml_file)
for test_filename in test_filenames:
test_xml_file = os.path.join(xml_path,test_filename) + '.xml'
# print(test_xml_file)
test_xml_files.append(test_xml_file)
print(test_xml_files)
print(train_xml_files)
# 3、转换数据
convert(test_xml_files, r'E:\COCO2017\test\annotations\test.json',mode="test") # 这里是生成的json保存位置，改一下
convert(train_xml_files, r'E:\COCO2017\train\annotations\train.json',mode="train") # 这里是生成的json保存位置，改一下
