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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import math
import os
import os.path as osp
import cv2
import mmcv
import mmengine
from PIL import Image
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 ann_file in os.listdir(gt_dir):
img_file = osp.join(img_dir, ann_file.replace('txt', 'jpg'))
# This dataset contains some images obtained from .gif,
# which cannot be loaded by mmcv.imread(), convert them
# to RGB mode.
try:
if mmcv.imread(img_file) is None:
print(f'Convert {img_file} to RGB mode.')
img = Image.open(img_file)
img = img.convert('RGB')
img.save(img_file)
except cv2.error:
print(f'Skip broken img {img_file}')
continue
ann_list.append(osp.join(gt_dir, ann_file))
imgs_list.append(img_file)
all_files = list(zip(imgs_list, 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)
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] == '.txt':
img_info = load_txt_info(gt_file, img_info)
else:
raise NotImplementedError
return img_info
def load_txt_info(gt_file, img_info):
"""Collect the annotation information.
The annotation format is as the following:
x1,y1,x2,y2,x3,y3,x4,y4,text
45.45,226.83,11.87,181.79,183.84,13.1,233.79,49.95,时尚袋袋
345.98,311.18,345.98,347.21,462.26,347.21,462.26,311.18,73774
462.26,292.34,461.44,299.71,502.39,299.71,502.39,292.34,73/74/737
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
"""
anno_info = []
with open(gt_file) as f:
lines = f.readlines()
for line in lines:
points = line.split(',')[0:8]
word = line.split(',')[8].rstrip('\n')
if word == '###':
continue
bbox = [math.floor(float(pt)) for pt in points]
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
"""
print('Cropping images...')
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
# Skip vertical texts
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 and val set of MTWI.')
parser.add_argument('root_path', help='Root dir path of MTWI')
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 MTWI 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 MTWI Val annotation'):
generate_ann(root_path, 'val', val_infos, args.preserve_vertical)
if __name__ == '__main__':
main()
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