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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import math | |
| import os | |
| import os.path as osp | |
| import xml.etree.ElementTree as ET | |
| import mmcv | |
| import mmengine | |
| from mmocr.utils import crop_img, dump_ocr_data | |
| def collect_files(img_dir, gt_dir, ratio): | |
| """Collect all images and their corresponding groundtruth files. | |
| Args: | |
| img_dir (str): The image directory | |
| gt_dir (str): The groundtruth directory | |
| ratio (float): Split ratio for val set | |
| 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(ratio, float) | |
| assert ratio < 1.0, 'val_ratio should be a float between 0.0 to 1.0' | |
| ann_list, imgs_list = [], [] | |
| for img_file in os.listdir(img_dir): | |
| ann_list.append(osp.join(gt_dir, img_file.split('.')[0] + '.xml')) | |
| imgs_list.append(osp.join(img_dir, img_file)) | |
| all_files = list(zip(sorted(imgs_list), sorted(ann_list))) | |
| assert len(all_files), f'No images found in {img_dir}' | |
| print(f'Loaded {len(all_files)} images from {img_dir}') | |
| trn_files, val_files = [], [] | |
| if ratio > 0: | |
| for i, file in enumerate(all_files): | |
| if i % math.floor(1 / ratio): | |
| trn_files.append(file) | |
| else: | |
| val_files.append(file) | |
| else: | |
| trn_files, val_files = all_files, [] | |
| print(f'training #{len(trn_files)}, val #{len(val_files)}') | |
| return trn_files, val_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] == '.xml': | |
| img_info = load_xml_info(gt_file, img_info) | |
| else: | |
| raise NotImplementedError | |
| return img_info | |
| def load_xml_info(gt_file, img_info): | |
| """Collect the annotation information. | |
| Annotation Format | |
| <image> | |
| <imageName>DSC02306.JPG</imageName> | |
| <resolution x="640" y="480" /> | |
| <words> | |
| <word x="61" y="140" width="566" height="107"> | |
| <character x="61" y="147" width="75" height="94" char="C" /> | |
| <character x="173" y="147" width="77" height="93" char="L" /> | |
| <character x="251" y="146" width="83" height="96" char="A" /> | |
| <character x="335" y="146" width="75" height="97" char="V" /> | |
| <character x="409" y="140" width="52" height="105" char="I" /> | |
| <character x="464" y="147" width="76" height="96" char="T" /> | |
| <character x="538" y="154" width="89" height="93" char="A" /> | |
| </word> | |
| </words> | |
| <illumination>no</illumination> | |
| <difficulty>2</difficulty> | |
| <tag> | |
| </tag> | |
| </image> | |
| 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 | |
| """ | |
| obj = ET.parse(gt_file) | |
| root = obj.getroot() | |
| anno_info = [] | |
| for word in root.iter('word'): | |
| x, y = max(0, int(word.attrib['x'])), max(0, int(word.attrib['y'])) | |
| w, h = int(word.attrib['width']), int(word.attrib['height']) | |
| bbox = [x, y, x + w, y, x + w, y + h, x, y + h] | |
| chars = [] | |
| for character in word.iter('character'): | |
| chars.append(character.attrib['char']) | |
| word = ''.join(chars) | |
| if len(word) == 0: | |
| continue | |
| 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 | |
| format (str): Annotation format, should be either 'txt' or 'jsonl' | |
| """ | |
| 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') | |
| 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'] | |
| 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 | |
| # Filter out vertical texts | |
| if not preserve_vertical and h / w > 2: | |
| 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 | |
| }] | |
| }) | |
| ensure_ascii = dict(ensure_ascii=False) | |
| dump_ocr_data(img_info, dst_label_file, 'textrecog', **ensure_ascii) | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='Generate training and val set of KAIST ') | |
| parser.add_argument('root_path', help='Root dir path of KAIST') | |
| parser.add_argument( | |
| '--val-ratio', help='Split ratio for val set', default=0.0, type=float) | |
| 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 | |
| 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 mmengine.Timer( | |
| print_tmpl='It takes {}s to convert KAIST Training annotation'): | |
| generate_ann(root_path, 'training', trn_infos, args.preserve_vertical) | |
| # Val set | |
| if len(val_files) > 0: | |
| val_infos = collect_annotations(val_files, nproc=args.nproc) | |
| with mmengine.Timer( | |
| print_tmpl='It takes {}s to convert KAIST Val annotation'): | |
| generate_ann(root_path, 'val', val_infos, args.preserve_vertical) | |
| if __name__ == '__main__': | |
| main() | |