| |
| 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 |
|
|
| |
| |
| suffixes = ['.png', '.PNG', '.jpg', '.JPG', '.jpeg', '.JPEG'] |
| |
|
|
| 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) |
|
|
| |
| 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) |
|
|
| |
| 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']) |
|
|
| |
| 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 |
| |
| img = mmcv.imread(img_file, 'unchanged') |
|
|
| split_name = osp.basename(osp.dirname(img_file)) |
| img_info = dict( |
| |
| file_name=osp.join(split_name, osp.basename(img_file)), |
| height=img.shape[0], |
| width=img.shape[1], |
| |
| 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() |
|
|
