# Copyright (c) OpenMMLab. All rights reserved. import argparse import glob import os.path as osp import xml.etree.ElementTree as ET from functools import partial import mmcv import numpy as np from shapely.geometry import Polygon from mmocr.utils import convert_annotations, list_from_file def collect_files(img_dir, gt_dir, split): """Collect all images and their corresponding groundtruth files. Args: img_dir(str): The image directory gt_dir(str): The groundtruth directory split(str): The split of dataset. Namely: training or test Returns: files(list): The list of tuples (img_file, groundtruth_file) """ assert isinstance(img_dir, str) assert img_dir assert isinstance(gt_dir, str) assert gt_dir # note that we handle png and jpg only. Pls convert others such as gif to # jpg or png offline suffixes = ['.png', '.PNG', '.jpg', '.JPG', '.jpeg', '.JPEG'] imgs_list = [] for suffix in suffixes: imgs_list.extend(glob.glob(osp.join(img_dir, '*' + suffix))) files = [] if split == 'training': for img_file in imgs_list: gt_file = gt_dir + '/' + osp.splitext( osp.basename(img_file))[0] + '.xml' files.append((img_file, gt_file)) assert len(files), f'No images found in {img_dir}' print(f'Loaded {len(files)} images from {img_dir}') elif split == 'test': for img_file in imgs_list: gt_file = gt_dir + '/000' + osp.splitext( osp.basename(img_file))[0] + '.txt' files.append((img_file, gt_file)) assert len(files), f'No images found in {img_dir}' print(f'Loaded {len(files)} images from {img_dir}') return files def collect_annotations(files, split, nproc=1): """Collect the annotation information. Args: files(list): The list of tuples (image_file, groundtruth_file) split(str): The split of dataset. Namely: training or test nproc(int): The number of process to collect annotations Returns: images(list): The list of image information dicts """ assert isinstance(files, list) assert isinstance(split, str) assert isinstance(nproc, int) load_img_info_with_split = partial(load_img_info, split=split) if nproc > 1: images = mmcv.track_parallel_progress( load_img_info_with_split, files, nproc=nproc) else: images = mmcv.track_progress(load_img_info_with_split, files) return images def load_txt_info(gt_file, img_info): anno_info = [] for line in list_from_file(gt_file): # each line has one ploygen (n vetices), and one text. # e.g., 695,885,866,888,867,1146,696,1143,####Latin 9 line = line.strip() strs = line.split(',') category_id = 1 assert strs[28][0] == '#' xy = [int(x) for x in strs[0:28]] assert len(xy) == 28 coordinates = np.array(xy).reshape(-1, 2) polygon = Polygon(coordinates) iscrowd = 0 area = polygon.area # convert to COCO style XYWH format min_x, min_y, max_x, max_y = polygon.bounds bbox = [min_x, min_y, max_x - min_x, max_y - min_y] text = strs[28][4:] anno = dict( iscrowd=iscrowd, category_id=category_id, bbox=bbox, area=area, text=text, segmentation=[xy]) anno_info.append(anno) img_info.update(anno_info=anno_info) return img_info def load_xml_info(gt_file, img_info): obj = ET.parse(gt_file) anno_info = [] for image in obj.getroot(): # image for box in image: # image h = box.attrib['height'] w = box.attrib['width'] x = box.attrib['left'] y = box.attrib['top'] text = box[0].text segs = box[1].text pts = segs.strip().split(',') pts = [int(x) for x in pts] assert len(pts) == 28 # pts = [] # for iter in range(2,len(box)): # pts.extend([int(box[iter].attrib['x']), # int(box[iter].attrib['y'])]) iscrowd = 0 category_id = 1 bbox = [int(x), int(y), int(w), int(h)] coordinates = np.array(pts).reshape(-1, 2) polygon = Polygon(coordinates) area = polygon.area anno = dict( iscrowd=iscrowd, category_id=category_id, bbox=bbox, area=area, text=text, segmentation=[pts]) anno_info.append(anno) img_info.update(anno_info=anno_info) return img_info def load_img_info(files, split): """Load the information of one image. Args: files(tuple): The tuple of (img_file, groundtruth_file) split(str): The split of dataset: training or test Returns: img_info(dict): The dict of the img and annotation information """ assert isinstance(files, tuple) assert isinstance(split, str) img_file, gt_file = files # read imgs with ignoring orientations img = mmcv.imread(img_file, 'unchanged') split_name = osp.basename(osp.dirname(img_file)) img_info = dict( # remove img_prefix for filename file_name=osp.join(split_name, osp.basename(img_file)), height=img.shape[0], width=img.shape[1], # anno_info=anno_info, segm_file=osp.join(split_name, osp.basename(gt_file))) if split == 'training': img_info = load_xml_info(gt_file, img_info) elif split == 'test': img_info = load_txt_info(gt_file, img_info) else: raise NotImplementedError return img_info def parse_args(): parser = argparse.ArgumentParser( description='Convert ctw1500 annotations to COCO format') parser.add_argument('root_path', help='ctw1500 root path') parser.add_argument('-o', '--out-dir', help='output path') parser.add_argument( '--split-list', nargs='+', help='a list of splits. e.g., "--split-list training test"') parser.add_argument( '--nproc', default=1, type=int, help='number of process') args = parser.parse_args() return args def main(): args = parse_args() root_path = args.root_path out_dir = args.out_dir if args.out_dir else root_path mmcv.mkdir_or_exist(out_dir) img_dir = osp.join(root_path, 'imgs') gt_dir = osp.join(root_path, 'annotations') set_name = {} for split in args.split_list: set_name.update({split: 'instances_' + split + '.json'}) assert osp.exists(osp.join(img_dir, split)) for split, json_name in set_name.items(): print(f'Converting {split} into {json_name}') with mmcv.Timer(print_tmpl='It takes {}s to convert icdar annotation'): files = collect_files( osp.join(img_dir, split), osp.join(gt_dir, split), split) image_infos = collect_annotations(files, split, nproc=args.nproc) convert_annotations(image_infos, osp.join(out_dir, json_name)) if __name__ == '__main__': main()