mirror of https://github.com/open-mmlab/mmocr.git
289 lines
9.4 KiB
Python
289 lines
9.4 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import json
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import os.path as osp
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import mmcv
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import mmengine
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import numpy as np
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from mmocr.utils.fileio import list_to_file
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from mmocr.utils.img_utils import crop_img
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def collect_files(img_dir, gt_dir, split_info):
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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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split_info (dict): The split information for train/val/test
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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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assert isinstance(split_info, dict)
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assert split_info
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ann_list, imgs_list = [], []
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for group in split_info:
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for img in split_info[group]:
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image_path = osp.join(img_dir, img)
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anno_path = osp.join(gt_dir, 'groups', group,
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img.replace('jpg', 'json'))
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# Filtering out the missing images
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if not osp.exists(image_path) or not osp.exists(anno_path):
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continue
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imgs_list.append(image_path)
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ann_list.append(anno_path)
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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 = mmengine.track_parallel_progress(
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load_img_info, files, nproc=nproc)
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else:
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images = mmengine.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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Annotation Format
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{
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'filedBBs': [{
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'poly_points': [[435,1406], [466,1406], [466,1439], [435,1439]],
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"type": "fieldCheckBox",
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"id": "f0",
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"isBlank": 1, # 0:text,1:handwriting,2:print,3:blank,4:signature,
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}], ...
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"transcriptions":{
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"f38": "CASE NUMBER",
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"f29": "July 1, 1949",
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"t20": "RANK",
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"t19": "COMPANY",
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...
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}
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}
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Some special characters are used in the transcription:
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"«text»" indicates that "text" had a strikethrough
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"¿" indicates the transcriber could not read a character
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"§" indicates the whole line or word was illegible
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"" (empty string) is if the field was blank
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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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assert isinstance(gt_file, str)
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assert isinstance(img_info, dict)
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annotation = mmengine.load(gt_file)
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anno_info = []
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# 'textBBs' contains the printed texts of the table while 'fieldBBs'
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# contains the text filled by human.
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for box_type in ['textBBs', 'fieldBBs']:
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# NAF dataset only provides transcription GT for 'filedBBs', the
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# 'textBBs' is only used for detection task.
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if box_type == 'textBBs':
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continue
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for anno in annotation[box_type]:
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# Skip images containing detection annotations only
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if 'transcriptions' not in annotation.keys():
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continue
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# Skip boxes without recognition GT
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if anno['id'] not in annotation['transcriptions'].keys():
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continue
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word = annotation['transcriptions'][anno['id']]
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# Skip blank boxes
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if len(word) == 0:
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continue
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bbox = np.array(anno['poly_points']).reshape(1, 8)[0].tolist()
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anno = dict(bbox=bbox, word=word)
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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 generate_ann(root_path, split, image_infos, preserve_vertical, format):
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"""Generate cropped annotations and label txt file.
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Args:
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root_path (str): The root path of the dataset
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split (str): The split of dataset. Namely: training or test
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image_infos (list[dict]): A list of dicts of the img and
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annotation information
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preserve_vertical (bool): Whether to preserve vertical texts
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format (str): Annotation format, should be either 'txt' or 'jsonl'
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"""
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dst_image_root = osp.join(root_path, 'crops', split)
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ignore_image_root = osp.join(root_path, 'ignores', split)
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if split == 'training':
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dst_label_file = osp.join(root_path, f'train_label.{format}')
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elif split == 'val':
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dst_label_file = osp.join(root_path, f'val_label.{format}')
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elif split == 'test':
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dst_label_file = osp.join(root_path, f'test_label.{format}')
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else:
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raise NotImplementedError
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mmengine.mkdir_or_exist(dst_image_root)
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mmengine.mkdir_or_exist(ignore_image_root)
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lines = []
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for image_info in image_infos:
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index = 1
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src_img_path = osp.join(root_path, 'imgs', image_info['file_name'])
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image = mmcv.imread(src_img_path)
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src_img_root = image_info['file_name'].split('.')[0]
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for anno in image_info['anno_info']:
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word = anno['word']
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word = word.strip('\u202a') # Remove unicode control character
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word = word.replace('»',
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'').replace('«',
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'') # Remove strikethrough flag
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dst_img = crop_img(image, anno['bbox'], 0, 0)
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h, w, _ = dst_img.shape
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dst_img_name = f'{src_img_root}_{index}.png'
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index += 1
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# Skip invalid and illegible annotations
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if min(dst_img.shape) == 0 or '§' in word or '¿' in word or len(
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word) == 0:
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continue
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# Skip vertical texts
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# (Do Not Filter For Val and Test Split)
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if (not preserve_vertical and h / w > 2) and split == 'training':
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dst_img_path = osp.join(ignore_image_root, dst_img_name)
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mmcv.imwrite(dst_img, dst_img_path)
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continue
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dst_img_path = osp.join(dst_image_root, dst_img_name)
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mmcv.imwrite(dst_img, dst_img_path)
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if format == 'txt':
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lines.append(f'{osp.basename(dst_image_root)}/{dst_img_name} '
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f'{word}')
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elif format == 'jsonl':
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lines.append(
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json.dumps(
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{
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'filename':
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f'{osp.basename(dst_image_root)}/{dst_img_name}',
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'text': word
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},
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ensure_ascii=False))
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else:
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raise NotImplementedError
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list_to_file(dst_label_file, lines)
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Generate training, val, and test set of NAF ')
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parser.add_argument('root_path', help='Root dir path of NAF')
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parser.add_argument(
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'--preserve-vertical',
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help='Preserve samples containing vertical texts',
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action='store_true')
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parser.add_argument(
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'--format',
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default='jsonl',
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help='Use jsonl or string to format annotations',
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choices=['jsonl', 'txt'])
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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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split_info = mmengine.load(
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osp.join(root_path, 'annotations', 'train_valid_test_split.json'))
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split_info['training'] = split_info.pop('train')
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split_info['val'] = split_info.pop('valid')
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for split in ['training', 'val', 'test']:
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print(f'Processing {split} set...')
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with mmengine.Timer(
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print_tmpl='It takes {}s to convert NAF 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_info[split])
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image_infos = collect_annotations(files, nproc=args.nproc)
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generate_ann(root_path, split, image_infos, args.preserve_vertical,
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args.format)
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if __name__ == '__main__':
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main()
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