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
	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()
