VOC2012数据集格式转化为YOLO格式

 下面是 VOC2012数据集的目录格式

├── VOCdevkit 
│         ├── VOC2012   数据集根目录
│               ├── Annotations  标注数据的xml格式数据的目录
│               ├── lImageSets  
│               ├   └── Main     训练集、验证集、测试集的标签 
                └── JPEGImages   图片的目录

 修改路径

首先要修改好数据集的目录：将voc_root = "/data/VOCdevkit"修改为自己voc数据集VOCdevkit的目录下。

# voc数据集根目录以及版本
voc_root = "/data/VOCdevkit"
voc_version = "VOC2012"

 找一个存放保存文件的目录。

# 转换后的文件保存目录
save_file_root = "./my_yolo_dataset"

自己存放的类别文件

# label标签对应json文件
label_json_path = './data/pascal_voc_classes.json'

 json文件下入类别对应好标签。

{
    "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
}

 生成的路径

 最后会生产一个my_yolo_dataset文件夹

├── my_yolo_dataset   数据集根目录
    ├── train         训练集数据的目录
        ├── images    jpg格式的图片
        └── labels     TXT文件，存放标签和坐标 例如 19 0.482 0.405333 0.828 0.752
    ├── val          验证集数据的目录
        ├── images    jpg格式的图片
        └── labels     TXT文件，存放标签和坐标 例如 19 0.482 0.405333 0.828 0.752

还会生成一个.name文件，存放着类别

aeroplane
bicycle
bird
boat
bottle
bus
car
cat
chair
cow
diningtable
dog
horse
motorbike
person
pottedplant
sheep
sofa
train
tvmonitor

 全部代码：

"""
本脚本有两个功能：
1.将voc数据集标注信息(.xml)转为yolo标注格式(.txt)，并将图像文件复制到相应文件夹
2.根据json标签文件，生成对应names标签(my_data_label.names)
"""
import os
from tqdm import tqdm
from lxml import etree
import json
import shutil


# voc数据集根目录以及版本
voc_root = "/data/VOCdevkit"
voc_version = "VOC2012"

# 转换的训练集以及验证集对应txt文件
train_txt = "train.txt"
val_txt = "val.txt"

# 转换后的文件保存目录
save_file_root = "./my_yolo_dataset"

# label标签对应json文件
label_json_path = './data/pascal_voc_classes.json'

# 拼接出voc的images目录，xml目录，txt目录
voc_images_path = os.path.join(voc_root, voc_version, "JPEGImages")
voc_xml_path = os.path.join(voc_root, voc_version, "Annotations")
train_txt_path = os.path.join(voc_root, voc_version, "ImageSets", "Main", train_txt)
val_txt_path = os.path.join(voc_root, voc_version, "ImageSets", "Main", val_txt)

# 检查文件/文件夹都是否存在
assert os.path.exists(voc_images_path), "VOC images path not exist..."
assert os.path.exists(voc_xml_path), "VOC xml path not exist..."
assert os.path.exists(train_txt_path), "VOC train txt file not exist..."
assert os.path.exists(val_txt_path), "VOC val txt file not exist..."
assert os.path.exists(label_json_path), "label_json_path does not exist..."
if os.path.exists(save_file_root) is False:
    os.makedirs(save_file_root)


def parse_xml_to_dict(xml):
    """
    将xml文件解析成字典形式，参考tensorflow的recursive_parse_xml_to_dict
    Args：
        xml: xml tree obtained by parsing XML file contents using lxml.etree

    Returns:
        Python dictionary holding XML contents.
    """

    if len(xml) == 0:  # 遍历到底层，直接返回tag对应的信息
        return {xml.tag: xml.text}

    result = {}
    for child in xml:
        child_result = parse_xml_to_dict(child)  # 递归遍历标签信息
        if child.tag != 'object':
            result[child.tag] = child_result[child.tag]
        else:
            if child.tag not in result:  # 因为object可能有多个，所以需要放入列表里
                result[child.tag] = []
            result[child.tag].append(child_result[child.tag])
    return {xml.tag: result}


def translate_info(file_names: list, save_root: str, class_dict: dict, train_val='train'):
    """
    将对应xml文件信息转为yolo中使用的txt文件信息
    :param file_names:
    :param save_root:
    :param class_dict:
    :param train_val:
    :return:
    """
    save_txt_path = os.path.join(save_root, train_val, "labels")
    if os.path.exists(save_txt_path) is False:
        os.makedirs(save_txt_path)
    save_images_path = os.path.join(save_root, train_val, "images")
    if os.path.exists(save_images_path) is False:
        os.makedirs(save_images_path)

    for file in tqdm(file_names, desc="translate {} file...".format(train_val)):
        # 检查下图像文件是否存在
        img_path = os.path.join(voc_images_path, file + ".jpg")
        assert os.path.exists(img_path), "file:{} not exist...".format(img_path)

        # 检查xml文件是否存在
        xml_path = os.path.join(voc_xml_path, file + ".xml")
        assert os.path.exists(xml_path), "file:{} not exist...".format(xml_path)

        # read xml
        with open(xml_path) as fid:
            xml_str = fid.read()
        xml = etree.fromstring(xml_str)
        data = parse_xml_to_dict(xml)["annotation"]
        img_height = int(data["size"]["height"])
        img_width = int(data["size"]["width"])

        # write object info into txt
        assert "object" in data.keys(), "file: '{}' lack of object key.".format(xml_path)
        if len(data["object"]) == 0:
            # 如果xml文件中没有目标就直接忽略该样本
            print("Warning: in '{}' xml, there are no objects.".format(xml_path))
            continue

        with open(os.path.join(save_txt_path, file + ".txt"), "w") as f:
            for index, obj in enumerate(data["object"]):
                # 获取每个object的box信息
                xmin = float(obj["bndbox"]["xmin"])
                xmax = float(obj["bndbox"]["xmax"])
                ymin = float(obj["bndbox"]["ymin"])
                ymax = float(obj["bndbox"]["ymax"])
                class_name = obj["name"]
                class_index = class_dict[class_name] - 1  # 目标id从0开始

                # 进一步检查数据，有的标注信息中可能有w或h为0的情况，这样的数据会导致计算回归loss为nan
                if xmax <= xmin or ymax <= ymin:
                    print("Warning: in '{}' xml, there are some bbox w/h <=0".format(xml_path))
                    continue

                # 将box信息转换到yolo格式
                xcenter = xmin + (xmax - xmin) / 2
                ycenter = ymin + (ymax - ymin) / 2
                w = xmax - xmin
                h = ymax - ymin

                # 绝对坐标转相对坐标，保存6位小数
                xcenter = round(xcenter / img_width, 6)
                ycenter = round(ycenter / img_height, 6)
                w = round(w / img_width, 6)
                h = round(h / img_height, 6)

                info = [str(i) for i in [class_index, xcenter, ycenter, w, h]]

                if index == 0:
                    f.write(" ".join(info))
                else:
                    f.write("\n" + " ".join(info))

        # copy image into save_images_path
        path_copy_to = os.path.join(save_images_path, img_path.split(os.sep)[-1])
        if os.path.exists(path_copy_to) is False:
            shutil.copyfile(img_path, path_copy_to)


def create_class_names(class_dict: dict):
    keys = class_dict.keys()
    with open("./data/my_data_label.names", "w") as w:
        for index, k in enumerate(keys):
            if index + 1 == len(keys):
                w.write(k)
            else:
                w.write(k + "\n")


def main():
    # read class_indict
    json_file = open(label_json_path, 'r')
    class_dict = json.load(json_file)

    # 读取train.txt中的所有行信息，删除空行
    with open(train_txt_path, "r") as r:
        train_file_names = [i for i in r.read().splitlines() if len(i.strip()) > 0]
    # voc信息转yolo，并将图像文件复制到相应文件夹
    translate_info(train_file_names, save_file_root, class_dict, "train")

    # 读取val.txt中的所有行信息，删除空行
    with open(val_txt_path, "r") as r:
        val_file_names = [i for i in r.read().splitlines() if len(i.strip()) > 0]
    # voc信息转yolo，并将图像文件复制到相应文件夹
    translate_info(val_file_names, save_file_root, class_dict, "val")

    # 创建my_data_label.names文件
    create_class_names(class_dict)


if __name__ == "__main__":
    main()