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# 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()