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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import math
import os
import os.path as osp
import mmcv
import mmengine
from mmocr.utils import 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):
ann_list.append(osp.join(gt_dir, ann_file))
imgs_list.append(osp.join(img_dir, ann_file.replace('txt', 'jpg')))
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, difficult, text
390,902,1856,902,1856,1225,390,1225,0,"金氏眼镜"
1875,1170,2149,1170,2149,1245,1875,1245,0,"创于1989"
2054,1277,2190,1277,2190,1323,2054,1323,0,"城建店"
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, encoding='utf-8-sig') as f:
lines = f.readlines()
for line in lines:
points = line.split(',')[0:8]
word = line.split(',')[9].rstrip('\n').strip('"')
difficult = 1 if line.split(',')[8] != '0' else 0
segmentation = [int(pt) for pt in points]
x = max(0, min(segmentation[0::2]))
y = max(0, min(segmentation[1::2]))
w = abs(max(segmentation[0::2]) - x)
h = abs(max(segmentation[1::2]) - y)
bbox = [x, y, w, h]
if word == '###' or difficult == 1:
iscrowd = 1
else:
iscrowd = 0
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 val set of RCTW.')
parser.add_argument('root_path', help='Root dir path of RCTW')
parser.add_argument(
'--val-ratio', help='Split ratio for val set', default=0.0, type=float)
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
with mmengine.Timer(
print_tmpl='It takes {}s to convert RCTW Training annotation'):
trn_infos = collect_annotations(trn_files, nproc=args.nproc)
dump_ocr_data(trn_infos, osp.join(root_path,
'instances_training.json'),
'textdet')
# Val set
if len(val_files) > 0:
with mmengine.Timer(
print_tmpl='It takes {}s to convert RCTW Val annotation'):
val_infos = collect_annotations(val_files, nproc=args.nproc)
dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'),
'textdet')
if __name__ == '__main__':
main()
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