# Copyright (c) OpenMMLab. All rights reserved. import argparse import os.path as osp import mmcv import mmengine import numpy as np from mmocr.utils import crop_img, dump_ocr_data def collect_files(img_dir, gt_dir, split_info): """Collect all images and their corresponding groundtruth files. Args: img_dir (str): The image directory gt_dir (str): The groundtruth directory split_info (dict): The split information for train/val/test 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 assert isinstance(split_info, dict) assert split_info ann_list, imgs_list = [], [] for group in split_info: for img in split_info[group]: image_path = osp.join(img_dir, img) anno_path = osp.join(gt_dir, 'groups', group, img.replace('jpg', 'json')) # Filtering out the missing images if not osp.exists(image_path) or not osp.exists(anno_path): continue imgs_list.append(image_path) ann_list.append(anno_path) 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(img_file).split( '.')[0] # Read imgs while ignoring orientations img = mmcv.imread(img_file, 'unchanged') img_info = dict( file_name=osp.join(osp.basename(img_file)), height=img.shape[0], width=img.shape[1], segm_file=osp.join(osp.basename(gt_file))) if osp.splitext(gt_file)[1] == '.json': img_info = load_json_info(gt_file, img_info) else: raise NotImplementedError return img_info def load_json_info(gt_file, img_info): """Collect the annotation information. Annotation Format { 'filedBBs': [{ 'poly_points': [[435,1406], [466,1406], [466,1439], [435,1439]], "type": "fieldCheckBox", "id": "f0", "isBlank": 1, # 0:text,1:handwriting,2:print,3:blank,4:signature, }], ... "transcriptions":{ "f38": "CASE NUMBER", "f29": "July 1, 1949", "t20": "RANK", "t19": "COMPANY", ... } } Some special characters are used in the transcription: "«text»" indicates that "text" had a strikethrough "¿" indicates the transcriber could not read a character "§" indicates the whole line or word was illegible "" (empty string) is if the field was blank 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 """ assert isinstance(gt_file, str) assert isinstance(img_info, dict) annotation = mmengine.load(gt_file) anno_info = [] # 'textBBs' contains the printed texts of the table while 'fieldBBs' # contains the text filled by human. for box_type in ['textBBs', 'fieldBBs']: # NAF dataset only provides transcription GT for 'filedBBs', the # 'textBBs' is only used for detection task. if box_type == 'textBBs': continue for anno in annotation[box_type]: # Skip images containing detection annotations only if 'transcriptions' not in annotation.keys(): continue # Skip boxes without recognition GT if anno['id'] not in annotation['transcriptions'].keys(): continue word = annotation['transcriptions'][anno['id']] # Skip blank boxes if len(word) == 0: continue bbox = np.array(anno['poly_points']).reshape(1, 8)[0].tolist() anno = dict(bbox=bbox, word=word) anno_info.append(anno) img_info.update(anno_info=anno_info) return img_info def generate_ann(root_path, split, image_infos, preserve_vertical): """Generate cropped annotations and label txt file. Args: root_path (str): The root path of the dataset split (str): The split of dataset. Namely: training or test image_infos (list[dict]): A list of dicts of the img and annotation information preserve_vertical (bool): Whether to preserve vertical texts """ dst_image_root = osp.join(root_path, 'crops', split) ignore_image_root = osp.join(root_path, 'ignores', split) if split == 'training': dst_label_file = osp.join(root_path, 'train_label.json') elif split == 'val': dst_label_file = osp.join(root_path, 'val_label.json') elif split == 'test': dst_label_file = osp.join(root_path, 'test_label.json') else: raise NotImplementedError mmengine.mkdir_or_exist(dst_image_root) mmengine.mkdir_or_exist(ignore_image_root) img_info = [] for image_info in image_infos: 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'] word = word.strip('\u202a') # Remove unicode control character word = word.replace('»', '').replace('«', '') # Remove strikethrough flag 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 and illegible annotations if min(dst_img.shape) == 0 or '§' in word or '¿' in word or len( word) == 0: continue # Skip vertical texts # (Do Not Filter For Val and Test Split) 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, dst_label_file, 'textrecog') def parse_args(): parser = argparse.ArgumentParser( description='Generate training, val, and test set of NAF ') parser.add_argument('root_path', help='Root dir path of NAF') parser.add_argument( '--preserve-vertical', help='Preserve samples containing vertical texts', action='store_true') parser.add_argument( '--nproc', default=1, type=int, help='Number of process') args = parser.parse_args() return args def main(): args = parse_args() root_path = args.root_path split_info = mmengine.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 mmengine.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) generate_ann(root_path, split, image_infos, args.preserve_vertical) if __name__ == '__main__': main()