| |
| 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] |
| |
| 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']: |
|
|
| |
| 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 |
|
|
| |
| if min(dst_img.shape) == 0: |
| continue |
| |
| 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() |
|
|
