# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from pathlib import Path

import mmcv
from mmcv import Config, DictAction

from mmdet.core.utils import mask2ndarray
from mmdet.core.visualization import imshow_det_bboxes
from mmdet.datasets.builder import build_dataset


def parse_args():
    parser = argparse.ArgumentParser(description='Browse a dataset')
    parser.add_argument('config', help='train config file path')
    parser.add_argument(
        '--skip-type',
        type=str,
        nargs='+',
        default=['DefaultFormatBundle', 'Normalize', 'Collect'],
        help='skip some useless pipeline')
    parser.add_argument(
        '--output-dir',
        default=None,
        type=str,
        help='If there is no display interface, you can save it')
    parser.add_argument('--not-show', default=False, action='store_true')
    parser.add_argument(
        '--show-interval',
        type=float,
        default=2,
        help='the interval of show (s)')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    args = parser.parse_args()
    return args


def retrieve_data_cfg(config_path, skip_type, cfg_options):
    cfg = Config.fromfile(config_path)
    if cfg_options is not None:
        cfg.merge_from_dict(cfg_options)
    # import modules from string list.
    if cfg.get('custom_imports', None):
        from mmcv.utils import import_modules_from_strings
        import_modules_from_strings(**cfg['custom_imports'])
    train_data_cfg = cfg.data.train
    while 'dataset' in train_data_cfg and train_data_cfg[
            'type'] != 'MultiImageMixDataset':
        train_data_cfg = train_data_cfg['dataset']
    train_data_cfg['pipeline'] = [
        x for x in train_data_cfg.pipeline if x['type'] not in skip_type
    ]

    return cfg


def main():
    args = parse_args()
    cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options)

    dataset = build_dataset(cfg.data.train)

    progress_bar = mmcv.ProgressBar(len(dataset))

    for item in dataset:
        filename = os.path.join(args.output_dir,
                                Path(item['filename']).name
                                ) if args.output_dir is not None else None

        gt_masks = item.get('gt_masks', None)
        if gt_masks is not None:
            gt_masks = mask2ndarray(gt_masks)

        imshow_det_bboxes(
            item['img'],
            item['gt_bboxes'],
            item['gt_labels'],
            gt_masks,
            class_names=dataset.CLASSES,
            show=not args.not_show,
            wait_time=args.show_interval,
            out_file=filename,
            bbox_color=(255, 102, 61),
            text_color=(255, 102, 61))

        progress_bar.update()


if __name__ == '__main__':
    main()

import argparse
import os
from pathlib import Path

import mmcv
from mmcv import Config, DictAction

from mmdet.core.utils import mask2ndarray
from mmdet.core.visualization import imshow_det_bboxes
from mmdet.datasets.builder import build_dataset


def parse_args():
    parser = argparse.ArgumentParser(description='Browse a dataset')
    parser.add_argument('config', help='train config file path')
    parser.add_argument(
        '--skip-type',
        type=str,
        nargs='+',
        default=['DefaultFormatBundle', 'Normalize', 'Collect'],
        help='skip some useless pipeline')
    parser.add_argument(
        '--output-dir',
        default=None,
        type=str,
        help='If there is no display interface, you can save it')
    parser.add_argument('--not-show', default=False, action='store_true')
    parser.add_argument(
        '--show-interval',
        type=float,
        default=2,
        help='the interval of show (s)')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    args = parser.parse_args()
    return args


def retrieve_data_cfg(config_path, skip_type, cfg_options):
    cfg = Config.fromfile(config_path)
    if cfg_options is not None:
        cfg.merge_from_dict(cfg_options)
    # import modules from string list.
    if cfg.get('custom_imports', None):
        from mmcv.utils import import_modules_from_strings
        import_modules_from_strings(**cfg['custom_imports'])
    train_data_cfg = cfg.data.train
    while 'dataset' in train_data_cfg and train_data_cfg[
            'type'] != 'MultiImageMixDataset':
        train_data_cfg = train_data_cfg['dataset']
    train_data_cfg['pipeline'] = [
        x for x in train_data_cfg.pipeline if x['type'] not in skip_type
    ]

    return cfg


def main():
    args = parse_args()
    cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options)

    dataset = build_dataset(cfg.data.train)

    progress_bar = mmcv.ProgressBar(len(dataset))

    for item in dataset:
        filename = os.path.join(args.output_dir,
                                Path(item['filename']).name
                                ) if args.output_dir is not None else None

        gt_masks = item.get('gt_masks', None)
        if gt_masks is not None:
            gt_masks = mask2ndarray(gt_masks)

        imshow_det_bboxes(
            item['img'],
            item['gt_bboxes'],
            item['gt_labels'],
            gt_masks,
            class_names=dataset.CLASSES,
            show=not args.not_show,
            wait_time=args.show_interval,
            out_file=filename,
            bbox_color=(255, 102, 61),
            text_color=(255, 102, 61))

        progress_bar.update()


if __name__ == '__main__':
    main()
