# Copyright (c) OpenMMLab. All rights reserved. import argparse import os import os.path as osp import mmcv import mmengine from mmocr.utils import crop_img, dump_ocr_data def collect_files(img_dir, gt_dir): """Collect all images and their corresponding groundtruth files. Args: img_dir (str): The image directory gt_dir (str): The groundtruth directory Returns: files (list): The list of tuples (img_file, groundtruth_file) """ assert isinstance(img_dir, str) assert img_dir assert isinstance(gt_dir, str) assert gt_dir ann_list, imgs_list = [], [] for img_file in os.listdir(img_dir): ann_file = img_file.split('_')[0] + '_gt_ocr.txt' ann_list.append(osp.join(gt_dir, ann_file)) imgs_list.append(osp.join(img_dir, img_file)) files = list(zip(imgs_list, ann_list)) assert len(files), f'No images found in {img_dir}' print(f'Loaded {len(files)} images from {img_dir}') return files def collect_annotations(files, nproc=1): """Collect the annotation information. Args: files (list): The list of tuples (image_file, groundtruth_file) nproc (int): The number of process to collect annotations Returns: images (list): The list of image information dicts """ assert isinstance(files, list) assert isinstance(nproc, int) if nproc > 1: images = mmengine.track_parallel_progress( load_img_info, files, nproc=nproc) else: images = mmengine.track_progress(load_img_info, files) return images def load_img_info(files): """Load the information of one image. Args: files (tuple): The tuple of (img_file, groundtruth_file) Returns: img_info (dict): The dict of the img and annotation information """ assert isinstance(files, tuple) img_file, gt_file = files assert osp.basename(gt_file).split('_')[0] == osp.basename(gt_file).split( '_')[0] # read imgs while ignoring orientations img = mmcv.imread(img_file, 'unchanged') img_info = dict( file_name=osp.basename(img_file), height=img.shape[0], width=img.shape[1], segm_file=osp.basename(gt_file)) if osp.splitext(gt_file)[1] == '.txt': img_info = load_txt_info(gt_file, img_info) else: raise NotImplementedError return img_info def load_txt_info(gt_file, img_info): """Collect the annotation information. The annotation format is as the following: x, y, w, h, text 977, 152, 16, 49, NOME 962, 143, 12, 323, APPINHANESI BLAZEK PASSOTTO 906, 446, 12, 94, 206940361 905, 641, 12, 44, SPTC Args: gt_file (str): The path to ground-truth img_info (dict): The dict of the img and annotation information Returns: img_info (dict): The dict of the img and annotation information """ with open(gt_file, encoding='latin1') as f: anno_info = [] for line in f: line = line.strip('\n') # Ignore hard samples if line[0] == '[' or line[0] == 'x': continue ann = line.split(',') bbox = ann[0:4] bbox = [int(coord) for coord in bbox] x, y, w, h = bbox # in case ',' exists in label word = ','.join(ann[4:]) if len(ann[4:]) > 1 else ann[4] # remove the initial space word = word.strip() bbox = [x, y, x + w, y, x + w, y + h, x, y + h] anno = dict(bbox=bbox, word=word) anno_info.append(anno) img_info.update(anno_info=anno_info) return img_info def split_train_val_list(full_list, val_ratio): """Split list by val_ratio. Args: full_list (list): List to be splited val_ratio (float): Split ratio for val set return: list(list, list): Train_list and val_list """ n_total = len(full_list) offset = int(n_total * val_ratio) if n_total == 0 or offset < 1: return [], full_list val_list = full_list[:offset] train_list = full_list[offset:] return [train_list, val_list] def generate_ann(root_path, image_infos, preserve_vertical, val_ratio, format): """Generate cropped annotations and label txt file. Args: root_path (str): The root path of the dataset image_infos (list[dict]): A list of dicts of the img and annotation information preserve_vertical (bool): Whether to preserve vertical texts val_ratio (float): Split ratio for val set format (str): Using jsonl(dict) or str to format annotations """ assert val_ratio <= 1. if val_ratio: image_infos = split_train_val_list(image_infos, val_ratio) splits = ['training', 'val'] else: image_infos = [image_infos] splits = ['training'] for i, split in enumerate(splits): dst_image_root = osp.join(root_path, 'crops', split) ignore_image_root = osp.join(root_path, 'ignores', split) os.makedirs(dst_image_root, exist_ok=True) img_info = [] for image_info in image_infos[i]: index = 1 src_img_path = osp.join(root_path, 'imgs', image_info['file_name']) image = mmcv.imread(src_img_path) src_img_root = image_info['file_name'].split('.')[0] for anno in image_info['anno_info']: word = anno['word'] dst_img = crop_img(image, anno['bbox'], 0, 0) h, w, _ = dst_img.shape dst_img_name = f'{src_img_root}_{index}.png' index += 1 # Skip invalid annotations if min(dst_img.shape) == 0: continue # Skip vertical texts if not preserve_vertical and h / w > 2 and split == 'training': dst_img_path = osp.join(ignore_image_root, dst_img_name) mmcv.imwrite(dst_img, dst_img_path) continue dst_img_path = osp.join(dst_image_root, dst_img_name) mmcv.imwrite(dst_img, dst_img_path) img_info.append({ 'file_name': dst_img_name, 'anno_info': [{ 'text': word }] }) dump_ocr_data(img_info, osp.join(root_path, f'{split.lower()}_label.json'), 'textrecog') def parse_args(): parser = argparse.ArgumentParser( description='Generate training and val set of BID ') parser.add_argument('root_path', help='Root dir path of BID') parser.add_argument( '--preserve-vertical', help='Preserve samples containing vertical texts', action='store_true') parser.add_argument( '--val-ratio', help='Split ratio for val set', default=0., type=float) parser.add_argument( '--nproc', default=1, type=int, help='Number of processes') args = parser.parse_args() return args def main(): args = parse_args() root_path = args.root_path with mmengine.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) generate_ann(root_path, image_infos, args.preserve_vertical, args.val_ratio, args.format) if __name__ == '__main__': main()