mirror of https://github.com/open-mmlab/mmocr.git
253 lines
7.9 KiB
Python
253 lines
7.9 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 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.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, ratio):
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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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ratio (float): Split ratio for val set
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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(ratio, float)
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assert ratio < 1.0, 'val_ratio should be a float between 0.0 to 1.0'
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ann_list, imgs_list = [], []
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for ann_file in os.listdir(gt_dir):
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ann_list.append(osp.join(gt_dir, ann_file))
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imgs_list.append(osp.join(img_dir, ann_file.replace('txt', 'jpg')))
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all_files = list(zip(imgs_list, ann_list))
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assert len(all_files), f'No images found in {img_dir}'
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print(f'Loaded {len(all_files)} images from {img_dir}')
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trn_files, val_files = [], []
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if ratio > 0:
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for i, file in enumerate(all_files):
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if i % math.floor(1 / ratio):
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trn_files.append(file)
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else:
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val_files.append(file)
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else:
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trn_files, val_files = all_files, []
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print(f'training #{len(trn_files)}, val #{len(val_files)}')
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return trn_files, val_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)
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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] == '.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, difficult, text
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390,902,1856,902,1856,1225,390,1225,0,"金氏眼镜"
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1875,1170,2149,1170,2149,1245,1875,1245,0,"创于1989"
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2054,1277,2190,1277,2190,1323,2054,1323,0,"城建店"
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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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anno_info = []
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with open(gt_file, encoding='utf-8-sig') as f:
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lines = f.readlines()
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for line in lines:
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points = line.split(',')[0:8]
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word = line.split(',')[9].rstrip('\n').strip('"')
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difficult = 1 if line.split(',')[8] != '0' else 0
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bbox = [int(pt) for pt in points]
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if word == '###' or difficult == 1:
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continue
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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 val
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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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mmcv.mkdir_or_exist(dst_image_root)
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mmcv.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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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 annotations
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if min(dst_img.shape) == 0:
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continue
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# Filter out vertical texts
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if not preserve_vertical and h / w > 2:
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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'crops/{dst_img_name} ' 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': f'crops/{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 and val set of RCTW.')
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parser.add_argument('root_path', help='Root dir path of RCTW')
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parser.add_argument(
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'--val-ratio', help='Split ratio for val set', default=0.0, type=float)
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parser.add_argument(
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'--nproc', default=1, type=int, help='Number of process')
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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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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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ratio = args.val_ratio
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trn_files, val_files = collect_files(
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osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), ratio)
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# Train set
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with mmcv.Timer(
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print_tmpl='It takes {}s to convert RCTW Training annotation'):
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trn_infos = collect_annotations(trn_files, nproc=args.nproc)
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generate_ann(root_path, 'training', trn_infos, args.preserve_vertical,
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args.format)
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# Val set
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if len(val_files) > 0:
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with mmcv.Timer(
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print_tmpl='It takes {}s to convert RCTW Val annotation'):
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val_infos = collect_annotations(val_files, nproc=args.nproc)
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generate_ann(root_path, 'val', val_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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