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# Copyright (c) OpenMMLab. All rights reserved. | |
import argparse | |
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): | |
"""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_file = 'gt_' + str(int(img_file[2:6])) + '.txt' | |
ann_list.append(osp.join(gt_dir, ann_file)) | |
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 int(osp.basename(gt_file)[3:-4]) == int( | |
osp.basename(img_file)[2:-4]) | |
# read imgs while ignoring orientations | |
img = mmcv.imread(img_file, 'unchanged') | |
img_info = dict( | |
file_name=osp.basename(img_file), | |
height=img.shape[0], | |
width=img.shape[1], | |
segm_file=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 | |
118,15,147,15,148,46,118,46,LƯỢNG | |
149,9,165,9,165,43,150,43,TỐT | |
167,9,180,9,179,43,167,42,ĐỂ | |
181,12,193,12,193,43,181,43,CÓ | |
195,13,215,14,215,46,196,46,VIỆC | |
217,13,237,14,239,47,217,46,LÀM, | |
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 | |
""" | |
with open(gt_file, encoding='utf-8') as f: | |
anno_info = [] | |
for line in f: | |
line = line.strip('\n') | |
ann = line.split(',') | |
bbox = ann[0:8] | |
word = line[len(','.join(bbox)) + 1:] | |
bbox = [int(coord) for coord in bbox] | |
segmentation = bbox | |
x_min = min(bbox[0], bbox[2], bbox[4], bbox[6]) | |
x_max = max(bbox[0], bbox[2], bbox[4], bbox[6]) | |
y_min = min(bbox[1], bbox[3], bbox[5], bbox[7]) | |
y_max = max(bbox[1], bbox[3], bbox[5], bbox[7]) | |
w = x_max - x_min | |
h = y_max - y_min | |
bbox = [x_min, y_min, w, h] | |
iscrowd = 1 if word == '###' else 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 test set of VinText ') | |
parser.add_argument('root_path', help='Root dir path of VinText') | |
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 | |
for split in ['training', 'test', 'unseen_test']: | |
print(f'Processing {split} set...') | |
with mmengine.Timer( | |
print_tmpl='It takes {}s to convert VinText annotation'): | |
files = collect_files( | |
osp.join(root_path, 'imgs', split), | |
osp.join(root_path, 'annotations')) | |
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() | |