voc数据集格式转coco数据集格式

做一个项目时，有时候不确定到底那个算法模型在我的数据集上表现最好，所以可能需要跑几个模型然后进行对比分析，但是很多模型对数据集的格式要求不同啊，每次都要被数据转换这个烦人的工作折磨很久……所以在空了的时候决定整理记录一下，也好方便自己下次使用。

本文参考了下面这篇帖子，亲测有效！！！非常感谢。。。将VOC格式的数据集转化为coco格式（python）_奔跑的小仙女的博客-CSDN博客_voc数据集转coco数集https://blog.csdn.net/qq_43211132/article/details/109508645

1.我的VOC数据集目录结构 

Annotations文件夹里存放的是.xml文件，每张图片的标签信息和位置信息

JPEGImages文件夹存放的是.jpg图片

2.转换后的coco数据集目录结构

annotations文件夹存放的是划分后的train,val的.json文件

train2017,val2017存放的是对应的.jpg图片 

3.转换的python代码 

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')