プロジェクトに取り組んでいるとき、どのアルゴリズム モデルが自分のデータ セットで最も優れたパフォーマンスを発揮するかわからない場合があるため、複数のモデルを実行して比較分析を行う必要がある場合がありますが、多くのモデルにはデータ セットの形式について異なる要件があります。面倒なデータ変換作業に長い間悩まされてきました… そこで、今度使えるように、暇なときに整理して記録しておくことにしました。
この記事は以下の投稿を参考にしています, プロテストは効果的！！！どうもありがとう。。。VOC 形式のデータセットを coco 形式に変換します (python 
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1. 私の VOC データセットのディレクトリ構造
Annotations フォルダーには、.xml ファイル、各画像のラベル情報、位置情報が保存されます。
JPEGImages フォルダーには .jpg 画像が保存されます
2. 変換された coco データセットのディレクトリ構造
annotations フォルダーには、分割された train および val .json ファイルが保存されます。
train2017、val2017 は、対応する .jpg 写真を保存します。
3. 変換されたPythonコード
import os
import random
import shutil
import sys
import json
import glob
import xml.etree.ElementTree as ET
"""
修改下面3个参数
1.val_files_num : 验证集的数量
2.test_files_num ：测试集的数量
3.voc_annotations : voc的annotations路径
"""
val_files_num = 64
test_files_num = 1
voc_annotations = r'D:\download\PPYOLOE_pytorch-master\VOC2007\Annotations/' # voc的annotations路径
split = voc_annotations.split('/')
coco_name = 'VOC2007'
main_path = r'D:\download\PPYOLOE_pytorch-master' + '/'
# print(main_path)
coco_path = os.path.join(main_path, coco_name + '_COCO/')
coco_images = os.path.join(main_path, coco_name + '_COCO/images')
coco_json_annotations = os.path.join(main_path, coco_name + '_COCO/annotations/')
xml_val = os.path.join(main_path, 'xml', 'xml_val/')
xml_test = os.path.join(main_path, 'xml/', 'xml_test/')
xml_train = os.path.join(main_path, 'xml/', 'xml_train/')
voc_images = os.path.join(main_path, coco_name, 'JPEGImages/')
# from https://www.php.cn/python-tutorials-424348.html
def mkdir(path):
path = path.strip()
path = path.rstrip("\\")
isExists = os.path.exists(path)
if not isExists:
os.makedirs(path)
print(path + ' ----- folder created')
return True
else:
print(path + ' ----- folder existed')
return False
# foler to make, please enter full path
mkdir(coco_path)
mkdir(coco_images)
mkdir(coco_json_annotations)
mkdir(xml_val)
mkdir(xml_test)
mkdir(xml_train)
# voc images copy to coco images
for i in os.listdir(voc_images):
img_path = os.path.join(voc_images + i)
shutil.copy(img_path, coco_images)
# voc images copy to coco images
for i in os.listdir(voc_annotations):
img_path = os.path.join(voc_annotations + i)
shutil.copy(img_path, xml_train)
print("\n\n %s files copied to %s" % (val_files_num, xml_val))
for i in range(val_files_num):
if len(os.listdir(xml_train)) > 0:
random_file = random.choice(os.listdir(xml_train))
# print("%d) %s"%(i+1,random_file))
source_file = "%s/%s" % (xml_train, random_file)
if random_file not in os.listdir(xml_val):
shutil.move(source_file, xml_val)
else:
random_file = random.choice(os.listdir(xml_train))
source_file = "%s/%s" % (xml_train, random_file)
shutil.move(source_file, xml_val)
else:
print('The folders are empty, please make sure there are enough %d file to move' % (val_files_num))
break
for i in range(test_files_num):
if len(os.listdir(xml_train)) > 0:
random_file = random.choice(os.listdir(xml_train))
# print("%d) %s"%(i+1,random_file))
source_file = "%s/%s" % (xml_train, random_file)
if random_file not in os.listdir(xml_test):
shutil.move(source_file, xml_test)
else:
random_file = random.choice(os.listdir(xml_train))
source_file = "%s/%s" % (xml_train, random_file)
shutil.move(source_file, xml_test)
else:
print('The folders are empty, please make sure there are enough %d file to move' % (val_files_num))
break
print("\n\n" + "*" * 27 + "[ Done ! Go check your file ]" + "*" * 28)
START_BOUNDING_BOX_ID = 1
PRE_DEFINE_CATEGORIES = None
"""
main code below are from
https://github.com/Tony607/voc2coco
"""
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 ValueError("Can not find %s in %s." % (name, root.tag))
if length > 0 and len(vars) != length:
raise ValueError(
"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 = filename.replace("\\", "/")
filename = os.path.splitext(os.path.basename(filename))[0]
return filename
except:
raise ValueError("Filename %s is supposed to be an integer." % (filename))
def get_categories(xml_files):
"""Generate category name to id mapping from a list of xml files.
Arguments:
xml_files {list} -- A list of xml file paths.
Returns:
dict -- category name to id mapping.
"""
classes_names = []
for xml_file in xml_files:
tree = ET.parse(xml_file)
root = tree.getroot()
for member in root.findall("object"):
classes_names.append(member[0].text)
classes_names = list(set(classes_names))
classes_names.sort()
return {name: i for i, name in enumerate(classes_names)}
def convert(xml_files, json_file):
json_dict = {"images": [], "type": "instances", "annotations": [], "categories": []}
if PRE_DEFINE_CATEGORIES is not None:
categories = PRE_DEFINE_CATEGORIES
else:
categories = get_categories(xml_files)
bnd_id = START_BOUNDING_BOX_ID
for xml_file in xml_files:
tree = ET.parse(xml_file)
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 ValueError("%d paths found in %s" % (len(path), xml_file))
## 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)
## Currently 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)
os.makedirs(os.path.dirname(json_file), exist_ok=True)
json_fp = open(json_file, "w")
json_str = json.dumps(json_dict)
json_fp.write(json_str)
json_fp.close()
xml_val_files = glob.glob(os.path.join(xml_val, "*.xml"))
xml_train_files = glob.glob(os.path.join(xml_train, "*.xml"))
convert(xml_val_files, coco_json_annotations + 'instances_val2017.json')
convert(xml_train_files, coco_json_annotations + 'instances_train2017.json')
val_images = os.listdir(xml_val)
tarin_images = os.listdir(xml_train)
# srcfile 需要复制、移动的文件
# dstpath 目的地址
def mymovefile(srcfile, dstpath): # 移动函数
if not os.path.isfile(srcfile):
print("%s not exist!" % (srcfile))
else:
fpath, fname = os.path.split(srcfile) # 分离文件名和路径
if not os.path.exists(dstpath):
os.makedirs(dstpath) # 创建路径
shutil.move(srcfile, dstpath + fname) # 移动文件
print("move %s -> %s" % (srcfile, dstpath + fname))
coco_train = os.path.join(main_path, coco_name + '_COCO')
# os.makedirs(coco_train+"\\train")
os.makedirs(coco_train + "\\val2017")
src_dir = coco_images
dst_dir = coco_train + "/val2017/" # 目的路径记得加斜杠
# src_file_list = coco_images
for val_images_wj in val_images:
val_images_wj = val_images_wj[:-4]
val_images_wj += '.jpg'
val_images_wj = coco_images + '/' + val_images_wj
mymovefile(val_images_wj, dst_dir)
# src_file_list = glob(src_dir + file_name)
train_dir_1 = os.path.join(main_path, coco_name + '_COCO/images')
train_dir_2 = os.path.join(main_path, coco_name + '_COCO/train2017')
if not os.path.exists(train_dir_1):
os.mkdir(train_dir_1)
srcDir = train_dir_1
dstDir = train_dir_2
os.rename(srcDir, dstDir)
shutil.rmtree(main_path + '/xml')