mirror of https://github.com/open-mmlab/mmocr.git
171 lines
4.9 KiB
Python
171 lines
4.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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import os.path as osp
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import mmcv
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from mmocr.utils import dump_ocr_data
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def collect_files(img_dir, gt_dir):
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"""Collect all images and their corresponding groundtruth files.
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Args:
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img_dir (str): The image directory
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gt_dir (str): The groundtruth directory
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Returns:
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files (list): The list of tuples (img_file, groundtruth_file)
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"""
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assert isinstance(img_dir, str)
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assert img_dir
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assert isinstance(gt_dir, str)
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assert gt_dir
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ann_list, imgs_list = [], []
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for img_file in os.listdir(img_dir):
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ann_file = 'gt_' + str(int(img_file[2:6])) + '.txt'
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ann_list.append(osp.join(gt_dir, ann_file))
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imgs_list.append(osp.join(img_dir, img_file))
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files = list(zip(imgs_list, ann_list))
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assert len(files), f'No images found in {img_dir}'
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print(f'Loaded {len(files)} images from {img_dir}')
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return files
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def collect_annotations(files, nproc=1):
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"""Collect the annotation information.
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Args:
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files (list): The list of tuples (image_file, groundtruth_file)
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nproc (int): The number of process to collect annotations
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Returns:
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images (list): The list of image information dicts
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"""
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assert isinstance(files, list)
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assert isinstance(nproc, int)
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if nproc > 1:
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images = mmcv.track_parallel_progress(
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load_img_info, files, nproc=nproc)
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else:
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images = mmcv.track_progress(load_img_info, files)
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return images
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def load_img_info(files):
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"""Load the information of one image.
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Args:
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files (tuple): The tuple of (img_file, groundtruth_file)
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Returns:
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img_info (dict): The dict of the img and annotation information
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"""
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assert isinstance(files, tuple)
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img_file, gt_file = files
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assert int(osp.basename(gt_file)[3:-4]) == int(
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osp.basename(img_file)[2:-4])
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# read imgs while ignoring orientations
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img = mmcv.imread(img_file, 'unchanged')
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img_info = dict(
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file_name=osp.basename(img_file),
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height=img.shape[0],
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width=img.shape[1],
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segm_file=osp.basename(gt_file))
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if osp.splitext(gt_file)[1] == '.txt':
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img_info = load_txt_info(gt_file, img_info)
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else:
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raise NotImplementedError
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return img_info
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def load_txt_info(gt_file, img_info):
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"""Collect the annotation information.
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The annotation format is as the following:
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x1,y1,x2,y2,x3,y3,x4,y4,text
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118,15,147,15,148,46,118,46,LƯỢNG
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149,9,165,9,165,43,150,43,TỐT
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167,9,180,9,179,43,167,42,ĐỂ
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181,12,193,12,193,43,181,43,CÓ
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195,13,215,14,215,46,196,46,VIỆC
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217,13,237,14,239,47,217,46,LÀM,
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Args:
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gt_file (str): The path to ground-truth
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img_info (dict): The dict of the img and annotation information
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Returns:
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img_info (dict): The dict of the img and annotation information
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"""
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with open(gt_file, encoding='utf-8') as f:
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anno_info = []
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for line in f:
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line = line.strip('\n')
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ann = line.split(',')
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bbox = ann[0:8]
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word = line[len(','.join(bbox)) + 1:]
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bbox = [int(coord) for coord in bbox]
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segmentation = bbox
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x_min = min(bbox[0], bbox[2], bbox[4], bbox[6])
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x_max = max(bbox[0], bbox[2], bbox[4], bbox[6])
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y_min = min(bbox[1], bbox[3], bbox[5], bbox[7])
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y_max = max(bbox[1], bbox[3], bbox[5], bbox[7])
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w = x_max - x_min
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h = y_max - y_min
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bbox = [x_min, y_min, w, h]
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iscrowd = 1 if word == '###' else 0
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anno = dict(
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iscrowd=iscrowd,
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category_id=1,
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bbox=bbox,
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area=w * h,
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segmentation=[segmentation])
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anno_info.append(anno)
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img_info.update(anno_info=anno_info)
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return img_info
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Generate training and test set of VinText ')
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parser.add_argument('root_path', help='Root dir path of VinText')
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parser.add_argument(
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'--nproc', default=1, type=int, help='Number of processes')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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root_path = args.root_path
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for split in ['training', 'test', 'unseen_test']:
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print(f'Processing {split} set...')
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with mmcv.Timer(
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print_tmpl='It takes {}s to convert VinText annotation'):
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files = collect_files(
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osp.join(root_path, 'imgs', split),
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osp.join(root_path, 'annotations'))
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image_infos = collect_annotations(files, nproc=args.nproc)
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dump_ocr_data(image_infos,
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osp.join(root_path, 'instances_' + split + '.json'),
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'textdet')
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if __name__ == '__main__':
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main()
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