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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
import mmengine
from mmocr.utils import dump_ocr_data
def collect_files(data_dir):
"""Collect all images and their corresponding groundtruth files.
Args:
data_dir (str): The directory to dataset
Returns:
files (list): The list of tuples (img_file, groundtruth_file)
"""
assert isinstance(data_dir, str)
assert data_dir
ann_list, imgs_list = [], []
for video_dir in os.listdir(data_dir):
for frame_dir in os.listdir(osp.join(data_dir, video_dir)):
crt_dir = osp.join(data_dir, video_dir, frame_dir)
if not osp.isdir(crt_dir):
continue
for crt_file in os.listdir(crt_dir):
if crt_file.endswith('xml'):
ann_path = osp.join(crt_dir, crt_file)
img_path = osp.join(crt_dir,
crt_file.replace('xml', 'png'))
if os.path.exists(img_path):
ann_list.append(ann_path)
imgs_list.append(img_path)
else:
continue
files = list(zip(imgs_list, ann_list))
assert len(files), f'No images found in {data_dir}'
print(f'Loaded {len(files)} images from {data_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_file = os.path.split(img_file)[-1]
img_info = dict(
file_name=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.
The annotation format is as the following:
<annotation>
<object>
<name>hierarchy</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>657</xmin>
<ymin>467</ymin>
<xmax>839</xmax>
<ymax>557</ymax>
</bndbox>
</object>
</annotation>
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 obj in root.iter('object'):
x = max(0, int(obj.find('bndbox').find('xmin').text))
y = max(0, int(obj.find('bndbox').find('ymin').text))
xmax = int(obj.find('bndbox').find('xmax').text)
ymax = int(obj.find('bndbox').find('ymax').text)
w, h = abs(xmax - x), abs(ymax - y)
bbox = [x, y, w, h]
segmentation = [x, y, x + w, y, x + w, y + h, x, y + 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 Lecture Video DB ')
parser.add_argument('root_path', help='Root dir path of Lecture Video DB')
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 ['train', 'val', 'test']:
print(f'Processing {split} set...')
with mmengine.Timer(
print_tmpl='It takes {}s to convert LV annotation'):
files = collect_files(osp.join(root_path, 'imgs', 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()
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