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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import math
import os
import os.path as osp
import mmcv
from mmocr.utils import convert_annotations
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']:
iscrowd = 1 if len(ann['text']) == 0 else 0
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, w, h]
segmentation = [x, y, x + w, y, x + w, y + h, x, y + h]
anno = dict(
iscrowd=iscrowd,
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 and test set of FUNSD ')
parser.add_argument('root_path', help='Root dir path of FUNSD')
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
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)
convert_annotations(
image_infos, osp.join(root_path,
'instances_' + split + '.json'))
if __name__ == '__main__':
main()
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