将VOC格式的数据转换为COCO格式
1.实现了将目标检测任务中使用的 Pascal VOC 格式标注数据转换为 COCO 格式标注数据，并生成两个 COCO 格式的 JSON 文件，用于训练和验证。
2.通过解析 XML 文件，提取图片信息、类别信息和目标框信息，并将这些数据添加到对应的 COCO 格式数据中。
3.使用随机数种子将数据按照 8:2 的比例划分为训练集和验证集，并将相应的图片拷贝到新的文件夹中。
4.将生成的 COCO 格式数据分别保存为两个 JSON 文件。这样转换后的数据格式更适合用于训练目标检测模型，尤其是对于使用 COCO 格式的深度学习框架。文章来源地址https://www.toymoban.com/news/detail-620949.html
import xml.etree.ElementTree as ET
import os
import json
import collections
import random
import shutil
# 设置随机数种子，可以是任意整数
random_seed = 42
# 设置随机数种子
random.seed(random_seed)
coco_train = dict()
coco_train['images'] = []
coco_train['type'] = 'instances'
coco_train['annotations'] = []
coco_train['categories'] = []
coco_val = dict()
coco_val['images'] = []
coco_val['type'] = 'instances'
coco_val['annotations'] = []
coco_val['categories'] = []
# category_set = dict()
image_set = set()
train_image_id = 1
val_image_id = 200000 # Assuming you have less than 200000 images
category_item_id = 1
annotation_id = 1
category_set = ['huaheng_ng', 'liuwen', 'gubao', 'huaheng_ok', 'qipi']
def split_list_by_ratio(input_list, ratio=0.8):
# 计算切分的索引位置
split_index = int(len(input_list) * ratio)
# 随机打乱列表
random.shuffle(input_list)
# 划分为两个列表并返回
return input_list[:split_index], input_list[split_index:]
def addCatItem(name):
'''
增加json格式中的categories部分
'''
global category_item_id
category_item = collections.OrderedDict()
category_item['supercategory'] = 'none'
category_item['id'] = category_item_id
category_item['name'] = name
coco_train['categories'].append(category_item)
coco_val['categories'].append(category_item)
category_item_id += 1
def addImgItem(file_name, size, img_suffixes, is_train):
global train_image_id # 声明变量为全局变量
global val_image_id # 声明变量为全局变量
# global image_id
if file_name is None:
raise Exception('Could not find filename tag in xml file.')
if size['width'] is None:
raise Exception('Could not find width tag in xml file.')
if size['height'] is None:
raise Exception('Could not find height tag in xml file.')
# image_item = dict() #按照一定的顺序，这里采用collections.OrderedDict()
image_item = collections.OrderedDict()
jpg_name = os.path.splitext(file_name)[0] + img_suffixes
image_item['file_name'] = jpg_name
image_item['width'] = size['width']
image_item['height'] = size['height']
# image_item['id'] = image_id
# coco['images'].append(image_item)
if is_train:
image_item['id'] = train_image_id
coco_train['images'].append(image_item)
image_id = train_image_id
train_image_id += 1
else:
image_item['id'] = val_image_id
coco_val['images'].append(image_item)
image_id = val_image_id
val_image_id += 1
image_set.add(jpg_name)
image_id = image_id + 1
return image_id
def addAnnoItem(object_name, image_id, category_id, bbox, is_train):
global annotation_id
# annotation_item = dict()
annotation_item = collections.OrderedDict()
annotation_item['segmentation'] = []
seg = []
# bbox[] is x,y,w,h
# left_top
seg.append(bbox[0])
seg.append(bbox[1])
# left_bottom
seg.append(bbox[0])
seg.append(bbox[1] + bbox[3])
# right_bottom
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1] + bbox[3])
# right_top
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1])
annotation_item['segmentation'].append(seg)
annotation_item['area'] = bbox[2] * bbox[3]
annotation_item['iscrowd'] = 0
annotation_item['image_id'] = image_id
annotation_item['bbox'] = bbox
annotation_item['category_id'] = category_id
annotation_item['id'] = annotation_id
annotation_item['ignore'] = 0
annotation_id += 1
# coco['annotations'].append(annotation_item)
if is_train:
coco_train['annotations'].append(annotation_item)
else:
coco_val['annotations'].append(annotation_item)
def parseXmlFiles(xml_path, xmllist, img_suffixes, is_train):
for f in xmllist:
if not f.endswith('.xml'):
continue
bndbox = dict()
size = dict()
current_image_id = None
current_category_id = None
file_name = None
size['width'] = None
size['height'] = None
size['depth'] = None
xml_file = os.path.join(xml_path, f)
print(xml_file)
tree = ET.parse(xml_file)
root = tree.getroot() # 抓根结点元素
if root.tag != 'annotation': # 根节点标签
raise Exception('pascal voc xml root element should be annotation, rather than {}'.format(root.tag))
# elem is <folder>, <filename>, <size>, <object>
for elem in root:
current_parent = elem.tag
current_sub = None
object_name = None
# elem.tag, elem.attrib，elem.text
if elem.tag == 'folder':
continue
if elem.tag == 'filename':
file_name = elem.text
if file_name in category_set:
raise Exception('file_name duplicated')
# add img item only after parse <size> tag
elif current_image_id is None and file_name is not None and size['width'] is not None:
if file_name not in image_set:
current_image_id = addImgItem(file_name, size, img_suffixes, is_train) # 图片信息
print('add image with {} and {}'.format(file_name, size))
else:
raise Exception('duplicated image: {}'.format(file_name))
# subelem is <width>, <height>, <depth>, <name>, <bndbox>
for subelem in elem:
bndbox['xmin'] = None
bndbox['xmax'] = None
bndbox['ymin'] = None
bndbox['ymax'] = None
current_sub = subelem.tag
if current_parent == 'object' and subelem.tag == 'name':
object_name = subelem.text
# if object_name not in category_set:
# current_category_id = addCatItem(object_name)
# else:
# current_category_id = category_set[object_name]
current_category_id = category_set.index(object_name) + 1 # index默认从0开始,但是json文件是从1开始，所以+1
elif current_parent == 'size':
if size[subelem.tag] is not None:
raise Exception('xml structure broken at size tag.')
size[subelem.tag] = int(subelem.text)
# option is <xmin>, <ymin>, <xmax>, <ymax>, when subelem is <bndbox>
for option in subelem:
if current_sub == 'bndbox':
if bndbox[option.tag] is not None:
raise Exception('xml structure corrupted at bndbox tag.')
bndbox[option.tag] = int(option.text)
# only after parse the <object> tag
if bndbox['xmin'] is not None:
if object_name is None:
raise Exception('xml structure broken at bndbox tag')
if current_image_id is None:
raise Exception('xml structure broken at bndbox tag')
if current_category_id is None:
raise Exception('xml structure broken at bndbox tag')
bbox = []
# x
bbox.append(bndbox['xmin'])
# y
bbox.append(bndbox['ymin'])
# w
bbox.append(bndbox['xmax'] - bndbox['xmin'])
# h
bbox.append(bndbox['ymax'] - bndbox['ymin'])
print(
'add annotation with {},{},{},{}'.format(object_name, current_image_id - 1, current_category_id,
bbox))
addAnnoItem(object_name, current_image_id - 1, current_category_id, bbox, is_train)
def copy_img(img_path, file_list, img_suffixes, new_folder):
# global train_image_id # 将train_image_id声明为全局变量
# global val_image_id # 将val_image_id声明为全局变量
parent_directory = os.path.dirname(img_path)
dest_folder = os.path.join(parent_directory, new_folder)
# 创建目标文件夹
if not os.path.exists(dest_folder):
os.makedirs(dest_folder)
for each_file in file_list:
file_prefix = os.path.splitext(each_file)[0]
old_img_path = os.path.join(img_path, file_prefix + img_suffixes)
new_img_path = os.path.join(dest_folder, file_prefix + img_suffixes)
shutil.copy(old_img_path, new_img_path)
# print(f'已拷贝图片到{new_img_path}')
# 更新image_id
# if new_folder == 'train':
# train_image_id += 1
# else:
# val_image_id += 1
def check_image_folder_suffix(folder_path):
# 获取文件夹中所有文件的后缀名，并将它们放入一个集合(set)中
file_suffixes = set()
for file_name in os.listdir(folder_path):
if os.path.isfile(os.path.join(folder_path, file_name)):
_, file_suffix = os.path.splitext(file_name)
file_suffixes.add(file_suffix)
# 检查集合中后缀名的数量，如果数量为1，则所有图片都是同一个后缀，返回后缀名，否则报错
assert len(file_suffixes) == 1, "图片文件夹中的后缀名不统一"
return file_suffixes.pop()
if __name__ == '__main__':
# 存放img和xml的文件夹
img_path = 'data/images'
xml_path = 'data/Annotations'
# 确保img文件夹中只有一种格式
img_suffixes = check_image_folder_suffix(img_path)
# 保存生成的coco格式的json路径
train_json_file = 'data/annotations_train.json'
val_json_file = 'data/annotations_val.json'
# 添加categories部分
for categoryname in category_set:
addCatItem(categoryname)
# 获取所有的XML文件列表
xmllist = os.listdir(xml_path)
# 按8:2的随机比例划分为两个列表
train_list, val_list = split_list_by_ratio(xmllist, ratio=0.8)
print(train_list)
print('--------------------')
print(val_list)
# 拷贝图片到新的文件夹
copy_img(img_path, train_list, img_suffixes, 'train')
copy_img(img_path, val_list, img_suffixes, 'val')
parseXmlFiles(xml_path, train_list, img_suffixes, True)
parseXmlFiles(xml_path, val_list, img_suffixes, False)
json.dump(coco_train, open(train_json_file, 'w'))
json.dump(coco_val, open(val_json_file, 'w'))
文章来源：https://www.toymoban.com/news/detail-620949.html
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