mirror of https://github.com/open-mmlab/mmocr.git
158 lines
4.4 KiB
Python
158 lines
4.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import math
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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 convert_annotations
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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 gt_file in os.listdir(gt_dir):
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ann_list.append(osp.join(gt_dir, gt_file))
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imgs_list.append(osp.join(img_dir, gt_file.replace('.json', '.png')))
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files = list(zip(sorted(imgs_list), sorted(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 osp.basename(gt_file).split('.')[0] == osp.basename(img_file).split(
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'.')[0]
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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.join(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.join(osp.basename(gt_file)))
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if osp.splitext(gt_file)[1] == '.json':
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img_info = load_json_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_json_info(gt_file, img_info):
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"""Collect the annotation information.
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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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annotation = mmcv.load(gt_file)
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anno_info = []
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for form in annotation['form']:
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for ann in form['words']:
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iscrowd = 1 if len(ann['text']) == 0 else 0
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x1, y1, x2, y2 = ann['box']
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x = max(0, min(math.floor(x1), math.floor(x2)))
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y = max(0, min(math.floor(y1), math.floor(y2)))
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w, h = math.ceil(abs(x2 - x1)), math.ceil(abs(y2 - y1))
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bbox = [x, y, w, h]
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segmentation = [x, y, x + w, y, x + w, y + h, x, y + h]
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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 FUNSD ')
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parser.add_argument('root_path', help='Root dir path of FUNSD')
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parser.add_argument(
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'--nproc', default=1, type=int, help='Number of process')
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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']:
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print(f'Processing {split} set...')
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with mmcv.Timer(print_tmpl='It takes {}s to convert FUNSD annotation'):
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files = collect_files(
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osp.join(root_path, 'imgs'),
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osp.join(root_path, 'annotations', split))
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image_infos = collect_annotations(files, nproc=args.nproc)
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convert_annotations(
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image_infos, osp.join(root_path,
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'instances_' + split + '.json'))
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if __name__ == '__main__':
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main()
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