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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import glob | |
| import os | |
| import os.path as osp | |
| import re | |
| import mmcv | |
| import numpy as np | |
| import scipy.io as scio | |
| import yaml | |
| from shapely.geometry import Polygon | |
| from mmocr.datasets.pipelines.crop import crop_img | |
| from mmocr.utils.fileio import list_to_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'] | |
| # suffixes = ['.png'] | |
| imgs_list = [] | |
| for suffix in suffixes: | |
| imgs_list.extend(glob.glob(osp.join(img_dir, '*' + suffix))) | |
| imgs_list = sorted(imgs_list) | |
| ann_list = sorted( | |
| [osp.join(gt_dir, gt_file) for gt_file in os.listdir(gt_dir)]) | |
| files = [(img_file, gt_file) | |
| for (img_file, gt_file) in zip(imgs_list, ann_list)] | |
| 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, nproc=1): | |
| """Collect the annotation information. | |
| Args: | |
| files(list): The list of tuples (image_file, groundtruth_file) | |
| nproc(int): The number of process to collect annotations | |
| Returns: | |
| images(list): The list of image information dicts | |
| """ | |
| assert isinstance(files, list) | |
| assert isinstance(nproc, int) | |
| if nproc > 1: | |
| images = mmcv.track_parallel_progress( | |
| load_img_info, files, nproc=nproc) | |
| else: | |
| images = mmcv.track_progress(load_img_info, files) | |
| return images | |
| def get_contours_mat(gt_path): | |
| """Get the contours and words for each ground_truth mat file. | |
| Args: | |
| gt_path(str): The relative path of the ground_truth mat file | |
| Returns: | |
| contours(list[lists]): A list of lists of contours | |
| for the text instances | |
| words(list[list]): A list of lists of words (string) | |
| for the text instances | |
| """ | |
| assert isinstance(gt_path, str) | |
| contours = [] | |
| words = [] | |
| data = scio.loadmat(gt_path) | |
| data_polygt = data['polygt'] | |
| for i, lines in enumerate(data_polygt): | |
| X = np.array(lines[1]) | |
| Y = np.array(lines[3]) | |
| point_num = len(X[0]) | |
| word = lines[4] | |
| if len(word) == 0: | |
| word = '???' | |
| else: | |
| word = word[0] | |
| if word == '#': | |
| word = '###' | |
| continue | |
| words.append(word) | |
| arr = np.concatenate([X, Y]).T | |
| contour = [] | |
| for i in range(point_num): | |
| contour.append(arr[i][0]) | |
| contour.append(arr[i][1]) | |
| contours.append(np.asarray(contour)) | |
| return contours, words | |
| def load_mat_info(img_info, gt_file): | |
| """Load the information of one ground truth in .mat format. | |
| Args: | |
| img_info(dict): The dict of only the image information | |
| gt_file(str): The relative path of the ground_truth mat | |
| file for one image | |
| Returns: | |
| img_info(dict): The dict of the img and annotation information | |
| """ | |
| assert isinstance(img_info, dict) | |
| assert isinstance(gt_file, str) | |
| contours, words = get_contours_mat(gt_file) | |
| anno_info = [] | |
| for contour, word in zip(contours, words): | |
| if contour.shape[0] == 2: | |
| continue | |
| coordinates = np.array(contour).reshape(-1, 2) | |
| polygon = Polygon(coordinates) | |
| # convert to COCO style XYWH format | |
| min_x, min_y, max_x, max_y = polygon.bounds | |
| bbox = [min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y] | |
| anno = dict(word=word, bbox=bbox) | |
| anno_info.append(anno) | |
| img_info.update(anno_info=anno_info) | |
| return img_info | |
| def process_line(line, contours, words): | |
| """Get the contours and words by processing each line in the gt file. | |
| Args: | |
| line(str): The line in gt file containing annotation info | |
| contours(list[lists]): A list of lists of contours | |
| for the text instances | |
| words(list[list]): A list of lists of words (string) | |
| for the text instances | |
| Returns: | |
| contours(list[lists]): A list of lists of contours | |
| for the text instances | |
| words(list[list]): A list of lists of words (string) | |
| for the text instances | |
| """ | |
| line = '{' + line.replace('[[', '[').replace(']]', ']') + '}' | |
| ann_dict = re.sub('([0-9]) +([0-9])', r'\1,\2', line) | |
| ann_dict = re.sub('([0-9]) +([ 0-9])', r'\1,\2', ann_dict) | |
| ann_dict = re.sub('([0-9]) -([0-9])', r'\1,-\2', ann_dict) | |
| ann_dict = ann_dict.replace("[u',']", "[u'#']") | |
| ann_dict = yaml.safe_load(ann_dict) | |
| X = np.array([ann_dict['x']]) | |
| Y = np.array([ann_dict['y']]) | |
| if len(ann_dict['transcriptions']) == 0: | |
| word = '???' | |
| else: | |
| word = ann_dict['transcriptions'][0] | |
| if len(ann_dict['transcriptions']) > 1: | |
| for ann_word in ann_dict['transcriptions'][1:]: | |
| word += ',' + ann_word | |
| word = str(eval(word)) | |
| words.append(word) | |
| point_num = len(X[0]) | |
| arr = np.concatenate([X, Y]).T | |
| contour = [] | |
| for i in range(point_num): | |
| contour.append(arr[i][0]) | |
| contour.append(arr[i][1]) | |
| contours.append(np.asarray(contour)) | |
| return contours, words | |
| def get_contours_txt(gt_path): | |
| """Get the contours and words for each ground_truth txt file. | |
| Args: | |
| gt_path(str): The relative path of the ground_truth mat file | |
| Returns: | |
| contours(list[lists]): A list of lists of contours | |
| for the text instances | |
| words(list[list]): A list of lists of words (string) | |
| for the text instances | |
| """ | |
| assert isinstance(gt_path, str) | |
| contours = [] | |
| words = [] | |
| with open(gt_path, 'r') as f: | |
| tmp_line = '' | |
| for idx, line in enumerate(f): | |
| line = line.strip() | |
| if idx == 0: | |
| tmp_line = line | |
| continue | |
| if not line.startswith('x:'): | |
| tmp_line += ' ' + line | |
| continue | |
| else: | |
| complete_line = tmp_line | |
| tmp_line = line | |
| contours, words = process_line(complete_line, contours, words) | |
| if tmp_line != '': | |
| contours, words = process_line(tmp_line, contours, words) | |
| for word in words: | |
| if word == '#': | |
| word = '###' | |
| continue | |
| return contours, words | |
| def load_txt_info(gt_file, img_info): | |
| """Load the information of one ground truth in .txt format. | |
| Args: | |
| img_info(dict): The dict of only the image information | |
| gt_file(str): The relative path of the ground_truth mat | |
| file for one image | |
| Returns: | |
| img_info(dict): The dict of the img and annotation information | |
| """ | |
| contours, words = get_contours_txt(gt_file) | |
| anno_info = [] | |
| for contour, word in zip(contours, words): | |
| if contour.shape[0] == 2: | |
| continue | |
| coordinates = np.array(contour).reshape(-1, 2) | |
| polygon = Polygon(coordinates) | |
| # convert to COCO style XYWH format | |
| min_x, min_y, max_x, max_y = polygon.bounds | |
| bbox = [min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y] | |
| anno = dict(word=word, bbox=bbox) | |
| anno_info.append(anno) | |
| img_info.update(anno_info=anno_info) | |
| return img_info | |
| def generate_ann(root_path, split, image_infos): | |
| """Generate cropped annotations and label txt file. | |
| Args: | |
| root_path(str): The relative path of the totaltext file | |
| split(str): The split of dataset. Namely: training or test | |
| image_infos(list[dict]): A list of dicts of the img and | |
| annotation information | |
| """ | |
| dst_image_root = osp.join(root_path, 'dst_imgs', split) | |
| if split == 'training': | |
| dst_label_file = osp.join(root_path, 'train_label.txt') | |
| elif split == 'test': | |
| dst_label_file = osp.join(root_path, 'test_label.txt') | |
| os.makedirs(dst_image_root, exist_ok=True) | |
| lines = [] | |
| for image_info in image_infos: | |
| index = 1 | |
| src_img_path = osp.join(root_path, 'imgs', image_info['file_name']) | |
| image = mmcv.imread(src_img_path) | |
| src_img_root = osp.splitext(image_info['file_name'])[0].split('/')[1] | |
| for anno in image_info['anno_info']: | |
| word = anno['word'] | |
| dst_img = crop_img(image, anno['bbox']) | |
| # Skip invalid annotations | |
| if min(dst_img.shape) == 0: | |
| continue | |
| dst_img_name = f'{src_img_root}_{index}.png' | |
| index += 1 | |
| dst_img_path = osp.join(dst_image_root, dst_img_name) | |
| mmcv.imwrite(dst_img, dst_img_path) | |
| lines.append(f'{osp.basename(dst_image_root)}/{dst_img_name} ' | |
| f'{word}') | |
| list_to_file(dst_label_file, lines) | |
| def load_img_info(files): | |
| """Load the information of one image. | |
| Args: | |
| files(tuple): The tuple of (img_file, groundtruth_file) | |
| Returns: | |
| img_info(dict): The dict of the img and annotation information | |
| """ | |
| assert isinstance(files, tuple) | |
| 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 osp.splitext(gt_file)[1] == '.mat': | |
| img_info = load_mat_info(img_info, gt_file) | |
| elif osp.splitext(gt_file)[1] == '.txt': | |
| img_info = load_txt_info(gt_file, img_info) | |
| else: | |
| raise NotImplementedError | |
| return img_info | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='Convert totaltext annotations to COCO format') | |
| parser.add_argument('root_path', help='totaltext 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 totaltext annotation'): | |
| files = collect_files( | |
| osp.join(img_dir, split), osp.join(gt_dir, split), split) | |
| image_infos = collect_annotations(files, nproc=args.nproc) | |
| generate_ann(root_path, split, image_infos) | |
| if __name__ == '__main__': | |
| main() | |