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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(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 img_file in os.listdir(img_dir): | |
ann_path = osp.join(gt_dir, img_file.split('.')[0] + '.xml') | |
if os.path.exists(ann_path): | |
ann_list.append(ann_path) | |
imgs_list.append(osp.join(img_dir, img_file)) | |
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') | |
try: | |
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))) | |
except AttributeError: | |
print(f'Skip broken img {img_file}') | |
return None | |
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: | |
<annotations> | |
... | |
<object> | |
<name>SMT</name> | |
<pose>Unspecified</pose> | |
<truncated>0</truncated> | |
<difficult>0</difficult> | |
<bndbox> | |
<xmin>157</xmin> | |
<ymin>294</ymin> | |
<xmax>237</xmax> | |
<ymax>357</ymax> | |
</bndbox> | |
<object> | |
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 object in root.iter('object'): | |
word = object.find('name').text | |
iscrowd = 1 if len(word) == 0 else 0 | |
x1 = int(object.find('bndbox').find('xmin').text) | |
y1 = int(object.find('bndbox').find('ymin').text) | |
x2 = int(object.find('bndbox').find('xmax').text) | |
y2 = int(object.find('bndbox').find('ymax').text) | |
x = max(0, min(x1, x2)) | |
y = max(0, min(y1, y2)) | |
w, h = abs(x2 - x1), abs(y2 - y1) | |
bbox = [x1, y1, 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 split_train_val_list(full_list, val_ratio): | |
"""Split list by val_ratio. | |
Args: | |
full_list (list): List to be split | |
val_ratio (float): Split ratio for val set | |
return: | |
list(list, list): Train_list and val_list | |
""" | |
n_total = len(full_list) | |
offset = int(n_total * val_ratio) | |
if n_total == 0 or offset < 1: | |
return [], full_list | |
val_list = full_list[:offset] | |
train_list = full_list[offset:] | |
return [train_list, val_list] | |
def parse_args(): | |
parser = argparse.ArgumentParser( | |
description='Generate training and val set of ILST ') | |
parser.add_argument('root_path', help='Root dir path of ILST') | |
parser.add_argument( | |
'--val-ratio', help='Split ratio for val set', default=0., type=float) | |
parser.add_argument( | |
'--nproc', default=1, type=int, help='Number of processes') | |
args = parser.parse_args() | |
return args | |
def main(): | |
args = parse_args() | |
root_path = args.root_path | |
with mmengine.Timer(print_tmpl='It takes {}s to convert ILST annotation'): | |
files = collect_files( | |
osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations')) | |
image_infos = collect_annotations(files, nproc=args.nproc) | |
if args.val_ratio: | |
image_infos = split_train_val_list(image_infos, args.val_ratio) | |
splits = ['training', 'val'] | |
else: | |
image_infos = [image_infos] | |
splits = ['training'] | |
for i, split in enumerate(splits): | |
dump_ocr_data( | |
list(filter(None, image_infos[i])), | |
osp.join(root_path, 'instances_' + split + '.json'), 'textdet') | |
if __name__ == '__main__': | |
main() | |