Spaces:
Runtime error
Runtime error
| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import json | |
| import math | |
| import os | |
| import os.path as osp | |
| import mmcv | |
| from mmocr.datasets.pipelines.crop import crop_img | |
| from mmocr.utils.fileio import list_to_file | |
| 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 gt_file in os.listdir(gt_dir): | |
| ann_list.append(osp.join(gt_dir, gt_file)) | |
| imgs_list.append(osp.join(img_dir, gt_file.replace('.json', '.png'))) | |
| files = list(zip(sorted(imgs_list), sorted(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 = mmcv.track_parallel_progress( | |
| load_img_info, files, nproc=nproc) | |
| else: | |
| images = mmcv.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. | |
| 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 | |
| """ | |
| annotation = mmcv.load(gt_file) | |
| anno_info = [] | |
| for form in annotation['form']: | |
| for ann in form['words']: | |
| # Ignore illegible samples | |
| if len(ann['text']) == 0: | |
| continue | |
| x1, y1, x2, y2 = ann['box'] | |
| x = max(0, min(math.floor(x1), math.floor(x2))) | |
| y = max(0, min(math.floor(y1), math.floor(y2))) | |
| w, h = math.ceil(abs(x2 - x1)), math.ceil(abs(y2 - y1)) | |
| bbox = [x, y, x + w, y, x + w, y + h, x, y + h] | |
| word = ann['text'] | |
| 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, format): | |
| """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): Using jsonl(dict) or str to format annotations | |
| """ | |
| dst_image_root = osp.join(root_path, 'dst_imgs', split) | |
| if split == 'training': | |
| dst_label_file = osp.join(root_path, 'train_label.txt') | |
| elif split == 'test': | |
| dst_label_file = osp.join(root_path, 'test_label.txt') | |
| os.makedirs(dst_image_root, exist_ok=True) | |
| lines = [] | |
| 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']) | |
| h, w, _ = dst_img.shape | |
| # Skip invalid annotations | |
| if min(dst_img.shape) == 0: | |
| continue | |
| # Skip vertical texts | |
| if not preserve_vertical and h / w > 2: | |
| continue | |
| dst_img_name = f'{src_img_root}_{index}.png' | |
| index += 1 | |
| dst_img_path = osp.join(dst_image_root, dst_img_name) | |
| mmcv.imwrite(dst_img, dst_img_path) | |
| if format == 'txt': | |
| lines.append(f'{osp.basename(dst_image_root)}/{dst_img_name} ' | |
| f'{word}') | |
| elif format == 'jsonl': | |
| lines.append( | |
| json.dumps({ | |
| 'filename': | |
| f'{osp.basename(dst_image_root)}/{dst_img_name}', | |
| 'text': word | |
| }), | |
| ensure_ascii=False) | |
| else: | |
| raise NotImplementedError | |
| list_to_file(dst_label_file, lines) | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='Generate training and test set of FUNSD ') | |
| parser.add_argument('root_path', help='Root dir path of FUNSD') | |
| 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 processes') | |
| parser.add_argument( | |
| '--format', | |
| default='jsonl', | |
| help='Use jsonl or string to format annotations', | |
| choices=['jsonl', 'txt']) | |
| args = parser.parse_args() | |
| return args | |
| def main(): | |
| args = parse_args() | |
| root_path = args.root_path | |
| for split in ['training', 'test']: | |
| print(f'Processing {split} set...') | |
| with mmcv.Timer(print_tmpl='It takes {}s to convert FUNSD annotation'): | |
| files = collect_files( | |
| osp.join(root_path, 'imgs'), | |
| osp.join(root_path, 'annotations', split)) | |
| image_infos = collect_annotations(files, nproc=args.nproc) | |
| generate_ann(root_path, split, image_infos, args.preserve_vertical, | |
| args.format) | |
| if __name__ == '__main__': | |
| main() | |