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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
from mmdet.models.utils import mask2ndarray
from mmdet.structures.bbox import BaseBoxes
from mmengine.config import Config, DictAction
from mmengine.registry import init_default_scope
from mmengine.utils import ProgressBar
from mmyolo.registry import DATASETS, VISUALIZERS
def parse_args():
parser = argparse.ArgumentParser(description='Browse a dataset')
parser.add_argument('config', help='train config file path')
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=0,
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 main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# register all modules in mmdet into the registries
init_default_scope(cfg.get('default_scope', 'mmyolo'))
dataset = DATASETS.build(cfg.train_dataloader.dataset)
visualizer = VISUALIZERS.build(cfg.visualizer)
visualizer.dataset_meta = dataset.metainfo
progress_bar = ProgressBar(len(dataset))
for item in dataset:
img = item['inputs'].permute(1, 2, 0).numpy()
data_sample = item['data_samples'].numpy()
gt_instances = data_sample.gt_instances
img_path = osp.basename(item['data_samples'].img_path)
out_file = osp.join(
args.output_dir,
osp.basename(img_path)) if args.output_dir is not None else None
img = img[..., [2, 1, 0]] # bgr to rgb
gt_bboxes = gt_instances.get('bboxes', None)
if gt_bboxes is not None and isinstance(gt_bboxes, BaseBoxes):
gt_instances.bboxes = gt_bboxes.tensor
gt_masks = gt_instances.get('masks', None)
if gt_masks is not None:
masks = mask2ndarray(gt_masks)
gt_instances.masks = masks.astype(bool)
data_sample.gt_instances = gt_instances
visualizer.add_datasample(
osp.basename(img_path),
img,
data_sample,
draw_pred=False,
show=not args.not_show,
wait_time=args.show_interval,
out_file=out_file)
progress_bar.update()
if __name__ == '__main__':
main()
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