third_party/mmyolo/tools/dataset_converters/balloon2coco.py

import os.path as osp

import mmcv
import mmengine


def convert_balloon_to_coco(ann_file, out_file, image_prefix):

    data_infos = mmengine.load(ann_file)

    annotations = []
    images = []
    obj_count = 0
    for idx, v in enumerate(mmengine.track_iter_progress(data_infos.values())):
        filename = v['filename']
        img_path = osp.join(image_prefix, filename)
        height, width = mmcv.imread(img_path).shape[:2]

        images.append(
            dict(id=idx, file_name=filename, height=height, width=width))

        for _, obj in v['regions'].items():
            assert not obj['region_attributes']
            obj = obj['shape_attributes']
            px = obj['all_points_x']
            py = obj['all_points_y']
            poly = [(x + 0.5, y + 0.5) for x, y in zip(px, py)]
            poly = [p for x in poly for p in x]

            x_min, y_min, x_max, y_max = (min(px), min(py), max(px), max(py))

            data_anno = dict(
                image_id=idx,
                id=obj_count,
                category_id=0,
                bbox=[x_min, y_min, x_max - x_min, y_max - y_min],
                area=(x_max - x_min) * (y_max - y_min),
                segmentation=[poly],
                iscrowd=0)
            annotations.append(data_anno)
            obj_count += 1

    coco_format_json = dict(
        images=images,
        annotations=annotations,
        categories=[{
            'id': 0,
            'name': 'balloon'
        }])
    mmengine.dump(coco_format_json, out_file)


if __name__ == '__main__':

    convert_balloon_to_coco('data/balloon/train/via_region_data.json',
                            'data/balloon/train.json', 'data/balloon/train/')
    convert_balloon_to_coco('data/balloon/val/via_region_data.json',
                            'data/balloon/val.json', 'data/balloon/val/')