RT-ODLab/tools/convert_ours_to_coco.py

import os
import json
import xml.etree.ElementTree as ET
import glob

import sys
sys.path.append("..")
from config.data_config.dataset_config import dataset_cfg
data_config = dataset_cfg["ourdataset"]
num_classes = data_config["num_classes"]
categories = data_config["class_names"]
START_BOUNDING_BOX_ID = 1
PRE_DEFINE_CATEGORIES = {categories[i]: i + 1 for i in range(num_classes)} 


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 int(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 i, xml_file in enumerate(xml_files):
        if i % 100 == 0:
            print('[{}] / [{}]'.format(i, len(xml_files)))
        tree = ET.parse(xml_file)
        root = tree.getroot()
        filename = get_and_check(root, "filename", 1).text
        ## 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()


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser(
        description="Convert VOC-style annotation labele by LabelImg to COCO format."
    )
    parser.add_argument("--root", help="Directory path to dataset.", type=str)
    parser.add_argument("--split", default='train', 
                        help="split of dataset.", type=str)
    parser.add_argument("-anno", "--annotations", default='annotations', 
                        help="Directory path to xml files.", type=str)
    parser.add_argument("-json", "--json_file", default='train.json',
                        help="Output COCO format json file.", type=str)
    args = parser.parse_args()

    data_dir = os.path.join(args.root, args.split)
    anno_path = os.path.join(data_dir, args.annotations)
    xml_files = glob.glob(os.path.join(anno_path, "*.xml"))
    json_file = os.path.join(data_dir, args.annotations, '{}.json'.format(args.split))
    print("Number of xml files: {}".format(len(xml_files)))
    print("Converting to COCO format ...")
    convert(xml_files, json_file)
    print("Success: {}".format(json_file))