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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import math
import os
import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import mmengine
from mmocr.utils import crop_img, dump_ocr_data
def collect_files(img_dir, gt_dir, ratio):
"""Collect all images and their corresponding groundtruth files.
Args:
img_dir (str): The image directory
gt_dir (str): The groundtruth directory
ratio (float): Split ratio for val set
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(ratio, float)
assert ratio < 1.0, 'val_ratio should be a float between 0.0 to 1.0'
ann_list, imgs_list = [], []
for img_file in os.listdir(img_dir):
ann_list.append(osp.join(gt_dir, img_file.split('.')[0] + '.xml'))
imgs_list.append(osp.join(img_dir, img_file))
all_files = list(zip(sorted(imgs_list), sorted(ann_list)))
assert len(all_files), f'No images found in {img_dir}'
print(f'Loaded {len(all_files)} images from {img_dir}')
trn_files, val_files = [], []
if ratio > 0:
for i, file in enumerate(all_files):
if i % math.floor(1 / ratio):
trn_files.append(file)
else:
val_files.append(file)
else:
trn_files, val_files = all_files, []
print(f'training #{len(trn_files)}, val #{len(val_files)}')
return trn_files, val_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] == '.xml':
img_info = load_xml_info(gt_file, img_info)
else:
raise NotImplementedError
return img_info
def load_xml_info(gt_file, img_info):
"""Collect the annotation information.
Annotation Format
<image>
<imageName>DSC02306.JPG</imageName>
<resolution x="640" y="480" />
<words>
<word x="61" y="140" width="566" height="107">
<character x="61" y="147" width="75" height="94" char="C" />
<character x="173" y="147" width="77" height="93" char="L" />
<character x="251" y="146" width="83" height="96" char="A" />
<character x="335" y="146" width="75" height="97" char="V" />
<character x="409" y="140" width="52" height="105" char="I" />
<character x="464" y="147" width="76" height="96" char="T" />
<character x="538" y="154" width="89" height="93" char="A" />
</word>
</words>
<illumination>no</illumination>
<difficulty>2</difficulty>
<tag>
</tag>
</image>
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
"""
obj = ET.parse(gt_file)
root = obj.getroot()
anno_info = []
for word in root.iter('word'):
x, y = max(0, int(word.attrib['x'])), max(0, int(word.attrib['y']))
w, h = int(word.attrib['width']), int(word.attrib['height'])
bbox = [x, y, x + w, y, x + w, y + h, x, y + h]
chars = []
for character in word.iter('character'):
chars.append(character.attrib['char'])
word = ''.join(chars)
if len(word) == 0:
continue
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
format (str): Annotation format, should be either 'txt' or 'jsonl'
"""
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')
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']
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 annotations
if min(dst_img.shape) == 0:
continue
# Filter out vertical texts
if not preserve_vertical and h / w > 2:
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
}]
})
ensure_ascii = dict(ensure_ascii=False)
dump_ocr_data(img_info, dst_label_file, 'textrecog', **ensure_ascii)
def parse_args():
parser = argparse.ArgumentParser(
description='Generate training and val set of KAIST ')
parser.add_argument('root_path', help='Root dir path of KAIST')
parser.add_argument(
'--val-ratio', help='Split ratio for val set', default=0.0, type=float)
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
ratio = args.val_ratio
trn_files, val_files = collect_files(
osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), ratio)
# Train set
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
with mmengine.Timer(
print_tmpl='It takes {}s to convert KAIST Training annotation'):
generate_ann(root_path, 'training', trn_infos, args.preserve_vertical)
# Val set
if len(val_files) > 0:
val_infos = collect_annotations(val_files, nproc=args.nproc)
with mmengine.Timer(
print_tmpl='It takes {}s to convert KAIST Val annotation'):
generate_ann(root_path, 'val', val_infos, args.preserve_vertical)
if __name__ == '__main__':
main()