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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import mmcv
import mmengine
from mmocr.utils import 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
{
'textBBs': [{
'poly_points': [[435,1406], [466,1406], [466,1439], [435,1439]],
"type": "text",
"id": "t1",
}], ...
}
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']:
for anno in annotation[box_type]:
# Skip blanks
if box_type == 'fieldBBs':
if anno['type'] == 'blank':
continue
xs, ys, segmentation = [], [], []
for p in anno['poly_points']:
xs.append(p[0])
ys.append(p[1])
segmentation.append(p[0])
segmentation.append(p[1])
x, y = max(0, min(xs)), max(0, min(ys))
w, h = max(xs) - x, max(ys) - y
bbox = [x, y, w, h]
anno = dict(
iscrowd=0,
category_id=1,
bbox=bbox,
area=w * h,
segmentation=[segmentation])
anno_info.append(anno)
img_info.update(anno_info=anno_info)
return img_info
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(
'--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)
dump_ocr_data(image_infos,
osp.join(root_path, 'instances_' + split + '.json'),
'textdet')
if __name__ == '__main__':
main()