def main(): args = parse_args() root_path = args.root_path print('Processing training set...') training_infos = collect_textocr_info(root_path, 'TextOCR_0.1_train.json') convert_annotations(training_infos, osp.join(root_path, 'instances_training.json')) print('Processing validation set...') val_infos = collect_textocr_info(root_path, 'TextOCR_0.1_val.json') convert_annotations(val_infos, osp.join(root_path, 'instances_val.json')) print('Finish')
def main(): args = parse_args() root_path = args.root_path print('Processing training set...') training_infos = collect_hiertext_info(root_path, args.level, 'train') convert_annotations(training_infos, osp.join(root_path, 'instances_training.json')) print('Processing validation set...') val_infos = collect_hiertext_info(root_path, args.level, 'val') convert_annotations(val_infos, osp.join(root_path, 'instances_val.json')) print('Finish')
def main(): args = parse_args() root_path = args.root_path for split in ['train', 'val', 'test']: print(f'Processing {split} set...') with mmcv.Timer(print_tmpl='It takes {}s to convert IMGUR annotation'): anno_infos = collect_imgur_info( root_path, f'imgur5k_annotations_{split}.json') convert_annotations(anno_infos, osp.join(root_path, f'instances_{split}.json'))
def main(): args = parse_args() root_path = args.root_path for split in ['train', 'val', 'test']: print(f'Processing {split} set...') with mmcv.Timer(print_tmpl='It takes {}s to convert LV annotation'): files = collect_files(osp.join(root_path, 'imgs', split)) image_infos = collect_annotations(files, nproc=args.nproc) convert_annotations( image_infos, osp.join(root_path, 'instances_' + split + '.json'))
def main(): args = parse_args() root_path = args.root_path with mmcv.Timer(print_tmpl='It takes {}s to convert BID annotation'): files = collect_files(osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations')) image_infos = collect_annotations(files, nproc=args.nproc) if args.val_ratio: image_infos = split_train_val_list(image_infos, args.val_ratio) splits = ['training', 'val'] else: image_infos = [image_infos] splits = ['training'] for i, split in enumerate(splits): convert_annotations( image_infos[i], osp.join(root_path, 'instances_' + split + '.json'))
def main(): args = parse_args() root_path = args.root_path split_info = mmcv.load( osp.join(root_path, 'annotations', 'train_valid_test_split.json')) split_info['training'] = split_info.pop('train') split_info['val'] = split_info.pop('valid') for split in ['training', 'val', 'test']: print(f'Processing {split} set...') with mmcv.Timer(print_tmpl='It takes {}s to convert NAF annotation'): files = collect_files(osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), split_info[split]) image_infos = collect_annotations(files, nproc=args.nproc) convert_annotations( image_infos, osp.join(root_path, 'instances_' + split + '.json'))
def main(): args = parse_args() root_path = args.root_path img_dir = osp.join(root_path, 'imgs') gt_dir = osp.join(root_path, 'annotations') set_name = {} for split in ['training', 'test']: set_name.update({split: 'instances_' + split + '.json'}) assert osp.exists(osp.join(img_dir, split)) for split, json_name in set_name.items(): print(f'Converting {split} into {json_name}') with mmcv.Timer( print_tmpl='It takes {}s to convert totaltext annotation'): files = collect_files(osp.join(img_dir, split), osp.join(gt_dir, split)) image_infos = collect_annotations(files, nproc=args.nproc) convert_annotations(image_infos, osp.join(root_path, json_name))
def main(): args = parse_args() root_path = args.root_path out_dir = args.out_dir if args.out_dir else root_path mmcv.mkdir_or_exist(out_dir) img_dir = osp.join(root_path, 'imgs') gt_dir = osp.join(root_path, 'annotations') set_name = {} for split in args.split_list: set_name.update({split: 'instances_' + split + '.json'}) assert osp.exists(osp.join(img_dir, split)) for split, json_name in set_name.items(): print(f'Converting {split} into {json_name}') with mmcv.Timer(print_tmpl='It takes {}s to convert icdar annotation'): files = collect_files(osp.join(img_dir, split), osp.join(gt_dir, split), split) image_infos = collect_annotations(files, split, nproc=args.nproc) convert_annotations(image_infos, osp.join(out_dir, json_name))
def main(): args = parse_args() root_path = args.root_path ratio = args.val_ratio trn_files, val_files = collect_files( osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), ratio) # Train set trn_infos = collect_annotations(trn_files, nproc=args.nproc) with mmcv.Timer( print_tmpl='It takes {}s to convert KAIST Training annotation'): convert_annotations(trn_infos, osp.join(root_path, 'instances_training.json')) # Val set if len(val_files) > 0: val_infos = collect_annotations(val_files, nproc=args.nproc) with mmcv.Timer( print_tmpl='It takes {}s to convert KAIST Val annotation'): convert_annotations(val_infos, osp.join(root_path, 'instances_val.json'))