mmsegmentation/tools/browse_dataset.py

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

import mmcv
import numpy as np
from mmcv import Config, DictAction

from mmseg.datasets import DATASETS


def parse_args():
    parser = argparse.ArgumentParser(description='Browse a dataset')
    parser.add_argument('config', help='train config file path')
    parser.add_argument(
        '--show-origin',
        default=False,
        action='store_true',
        help='if True, omit all augmentation in pipeline,'
        ' show origin image and seg map')
    parser.add_argument(
        '--skip-type',
        type=str,
        nargs='+',
        default=['DefaultFormatBundle', 'Normalize', 'Collect'],
        help='skip some useless pipeline，if `show-origin` is true, '
        'all pipeline except `Load` will be skipped')
    parser.add_argument(
        '--output-dir',
        default='./output',
        type=str,
        help='If there is no display interface, you can save it')
    parser.add_argument('--show', default=False, action='store_true')
    parser.add_argument(
        '--show-interval',
        type=int,
        default=999,
        help='the interval of show (ms)')
    parser.add_argument(
        '--opacity',
        type=float,
        default=0.5,
        help='the opacity of semantic map')
    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 imshow_semantic(img,
                    seg,
                    class_names,
                    palette=None,
                    win_name='',
                    show=False,
                    wait_time=0,
                    out_file=None,
                    opacity=0.5):
    """Draw `result` over `img`.

    Args:
        img (str or Tensor): The image to be displayed.
        seg (Tensor): The semantic segmentation results to draw over
            `img`.
        class_names (list[str]): Names of each classes.
        palette (list[list[int]]] | np.ndarray | None): The palette of
            segmentation map. If None is given, random palette will be
            generated. Default: None
        win_name (str): The window name.
        wait_time (int): Value of waitKey param.
            Default: 0.
        show (bool): Whether to show the image.
            Default: False.
        out_file (str or None): The filename to write the image.
            Default: None.
        opacity(float): Opacity of painted segmentation map.
            Default 0.5.
            Must be in (0, 1] range.
    Returns:
        img (Tensor): Only if not `show` or `out_file`
    """
    img = mmcv.imread(img)
    img = img.copy()
    if palette is None:
        palette = np.random.randint(0, 255, size=(len(class_names), 3))
    palette = np.array(palette)
    assert palette.shape[0] == len(class_names)
    assert palette.shape[1] == 3
    assert len(palette.shape) == 2
    assert 0 < opacity <= 1.0
    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
    for label, color in enumerate(palette):
        color_seg[seg == label, :] = color
    # convert to BGR
    color_seg = color_seg[..., ::-1]

    img = img * (1 - opacity) + color_seg * opacity
    img = img.astype(np.uint8)
    # if out_file specified, do not show image in window
    if out_file is not None:
        show = False

    if show:
        mmcv.imshow(img, win_name, wait_time)
    if out_file is not None:
        mmcv.imwrite(img, out_file)

    if not (show or out_file):
        warnings.warn('show==False and out_file is not specified, only '
                      'result image will be returned')
        return img


def _retrieve_data_cfg(_data_cfg, skip_type, show_origin):
    if show_origin is True:
        # only keep pipeline of Loading data and ann
        _data_cfg['pipeline'] = [
            x for x in _data_cfg.pipeline if 'Load' in x['type']
        ]
    else:
        _data_cfg['pipeline'] = [
            x for x in _data_cfg.pipeline if x['type'] not in skip_type
        ]


def retrieve_data_cfg(config_path, skip_type, cfg_options, show_origin=False):
    cfg = Config.fromfile(config_path)
    if cfg_options is not None:
        cfg.merge_from_dict(cfg_options)
    train_data_cfg = cfg.data.train
    if isinstance(train_data_cfg, list):
        for _data_cfg in train_data_cfg:
            while 'dataset' in _data_cfg and _data_cfg[
                    'type'] != 'MultiImageMixDataset':
                _data_cfg = _data_cfg['dataset']
            if 'pipeline' in _data_cfg:
                _retrieve_data_cfg(_data_cfg, skip_type, show_origin)
            else:
                raise ValueError
    else:
        while 'dataset' in train_data_cfg and train_data_cfg[
                'type'] != 'MultiImageMixDataset':
            train_data_cfg = train_data_cfg['dataset']
        _retrieve_data_cfg(train_data_cfg, skip_type, show_origin)
    return cfg


def main():
    args = parse_args()
    cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options,
                            args.show_origin)
    dataset = DATASETS.build(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
        imshow_semantic(
            item['img'],
            item['gt_semantic_seg'],
            dataset.CLASSES,
            dataset.PALETTE,
            show=args.show,
            wait_time=args.show_interval,
            out_file=filename,
            opacity=args.opacity,
        )
        progress_bar.update()


if __name__ == '__main__':
    main()