mirror of https://github.com/open-mmlab/mmocr.git
203 lines
6.0 KiB
Python
203 lines
6.0 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 cv2
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import mmcv
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from PIL import Image
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from mmocr.utils import dump_ocr_data
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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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img_file = osp.join(img_dir, ann_file.replace('txt', 'jpg'))
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# This dataset contains some images obtained from .gif,
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# which cannot be loaded by mmcv.imread(), convert them
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# to RGB mode.
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try:
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if mmcv.imread(img_file) is None:
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print(f'Convert {img_file} to RGB mode.')
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img = Image.open(img_file)
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img = img.convert('RGB')
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img.save(img_file)
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except cv2.error:
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print(f'Skip broken img {img_file}')
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continue
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ann_list.append(osp.join(gt_dir, ann_file))
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imgs_list.append(img_file)
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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,text
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45.45,226.83,11.87,181.79,183.84,13.1,233.79,49.95,时尚袋袋
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345.98,311.18,345.98,347.21,462.26,347.21,462.26,311.18,73774
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462.26,292.34,461.44,299.71,502.39,299.71,502.39,292.34,73/74/737
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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) 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(',')[8].rstrip('\n')
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segmentation = [math.floor(float(pt)) for pt in points]
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x = max(0, min(segmentation[0::2]))
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y = max(0, min(segmentation[1::2]))
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w = abs(max(segmentation[0::2]) - x)
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h = abs(max(segmentation[1::2]) - y)
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bbox = [x, y, w, h]
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anno = dict(
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iscrowd=1 if word == '###' else 0,
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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 val set of MTWI.')
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parser.add_argument('root_path', help='Root dir path of MTWI')
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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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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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trn_infos = collect_annotations(trn_files, nproc=args.nproc)
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with mmcv.Timer(
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print_tmpl='It takes {}s to convert MTWI Training annotation'):
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dump_ocr_data(trn_infos, osp.join(root_path,
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'instances_training.json'),
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'textdet')
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# Val set
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if len(val_files) > 0:
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val_infos = collect_annotations(val_files, nproc=args.nproc)
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with mmcv.Timer(
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print_tmpl='It takes {}s to convert MTWI Val annotation'):
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dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'),
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'textdet')
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if __name__ == '__main__':
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main()
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