在训练目标检测模型的时候一般使用labelimg标注的图像生产.xml格式的标注文件。有时候需要用到coco格式的json标注文件，在github找到了一个xml转json的脚本。(https://github.com/CivilNet/Gemfield/blob/master/src/python/pascal_voc_xml2json/pascal_voc_xml2json.py)

执行该脚本会读取Annotations下的.xml文件并解析其中的类别及boundbox的坐标，最后生成instances.json的文件。

这里使用了4张图像的xml进行测试。图像名字为2007_000027.jpg 2007_000032.jpg 2007_000033.jpg 2007_000039.jpg。

如下图所示为instances.json文件内容。从下图可以看到，coco的json标注格式实际上是一个大字典{}，里面包括了“images”,"annotations","type","categories"等信息(为了便于观察，图中画出的双箭头表示该属性从开始到结束的范围)。"images"存放每个图像的名字宽高及图像id，"annotations"存放对应相同图像id的图像box的四个坐标位置及该框的类别id，"categories"则表示每个类别id到该类真实名字的对应关系。

原git的脚本不支持划分训练集测试集，也不能提前设定标签的索引。所以进行了改动，如下：

#coding:utf-8

# pip install lxml

import os
import glob
import json
import shutil
import numpy as np
import xml.etree.ElementTree as ET



path2 = "."


START_BOUNDING_BOX_ID = 1


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_list, json_file):
    json_dict = {"images": [], "type": "instances", "annotations": [], "categories": []}
    categories = pre_define_categories.copy()
    bnd_id = START_BOUNDING_BOX_ID
    all_categories = {}
    for index, line in enumerate(xml_list):
        # print("Processing %s"%(line))
        xml_f = line
        tree = ET.parse(xml_f)
        root = tree.getroot()
        
        filename = os.path.basename(xml_f)[:-4] + ".jpg"
        image_id = 20190000001 + index
        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)
        ## 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 in all_categories:
                all_categories[category] += 1
            else:
                all_categories[category] = 1
            if category not in categories:
                if only_care_pre_define_categories:
                    continue
                new_id = len(categories) + 1
                print("[warning] category '{}' not in 'pre_define_categories'({}), create new id: {} automatically".format(category, pre_define_categories, new_id))
                categories[category] = new_id
            category_id = categories[category]
            bndbox = get_and_check(obj, 'bndbox', 1)
            xmin = int(float(get_and_check(bndbox, 'xmin', 1).text))
            ymin = int(float(get_and_check(bndbox, 'ymin', 1).text))
            xmax = int(float(get_and_check(bndbox, 'xmax', 1).text))
            ymax = int(float(get_and_check(bndbox, 'ymax', 1).text))
            assert(xmax > xmin), "xmax <= xmin, {}".format(line)
            assert(ymax > ymin), "ymax <= ymin, {}".format(line)
            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)
    json_fp = open(json_file, 'w')
    json_str = json.dumps(json_dict)
    json_fp.write(json_str)
    json_fp.close()
    print("------------create {} done--------------".format(json_file))
    print("find {} categories: {} -->>> your pre_define_categories {}: {}".format(len(all_categories), all_categories.keys(), len(pre_define_categories), pre_define_categories.keys()))
    print("category: id --> {}".format(categories))
    print(categories.keys())
    print(categories.values())


if __name__ == '__main__':
    classes = ['bicycle', 'pottedplant', 'tvmonitor']
    pre_define_categories = {}
    for i, cls in enumerate(classes):
        pre_define_categories[cls] = i + 1
    # pre_define_categories = {'a1': 1, 'a3': 2, 'a6': 3, 'a9': 4, "a10": 5}
    only_care_pre_define_categories = True
    # only_care_pre_define_categories = False

    train_ratio = 0.9
    save_json_train = 'instances_train2014.json'
    save_json_val = 'instances_val2014.json'
    xml_dir = "./tmp_xml"

    xml_list = glob.glob(xml_dir + "/*.xml")
    xml_list = np.sort(xml_list)
    np.random.seed(100)
    np.random.shuffle(xml_list)

    train_num = int(len(xml_list)*train_ratio)
    xml_list_train = xml_list[:train_num]
    xml_list_val = xml_list[train_num:]

    convert(xml_list_train, save_json_train)
    convert(xml_list_val, save_json_val)

    if os.path.exists(path2 + "/annotations"):
        shutil.rmtree(path2 + "/annotations")
    os.makedirs(path2 + "/annotations")
    if os.path.exists(path2 + "/images/train2014"):
        shutil.rmtree(path2 + "/images/train2014")
    os.makedirs(path2 + "/images/train2014")
    if os.path.exists(path2 + "/images/val2014"):
        shutil.rmtree(path2 +"/images/val2014")
    os.makedirs(path2 + "/images/val2014")

    f1 = open("train.txt", "w")
    for xml in xml_list_train:
        img = xml[:-4] + ".jpg"
        f1.write(os.path.basename(xml)[:-4] + "\n")
        shutil.copyfile(img, path2 + "/images/train2014/" + os.path.basename(img))

    f2 = open("test.txt", "w")
    for xml in xml_list_val:
        img = xml[:-4] + ".jpg"
        f2.write(os.path.basename(xml)[:-4] + "\n") 
        shutil.copyfile(img, path2 + "/images/val2014/" + os.path.basename(img))
    f1.close()
    f2.close()
    print("-------------------------------")
    print("train number:", len(xml_list_train))
    print("val number:", len(xml_list_val))