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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import math | |
| import os | |
| import os.path as osp | |
| import xml.etree.ElementTree as ET | |
| import mmcv | |
| import mmengine | |
| from mmocr.utils import dump_ocr_data | |
| def collect_files(img_dir, gt_dir, ratio): | |
| """Collect all images and their corresponding groundtruth files. | |
| Args: | |
| img_dir (str): The image directory | |
| gt_dir (str): The groundtruth directory | |
| ratio (float): Split ratio for val set | |
| 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 | |
| assert isinstance(ratio, float) | |
| assert ratio < 1.0, 'val_ratio should be a float between 0.0 to 1.0' | |
| ann_list, imgs_list = [], [] | |
| for img_file in os.listdir(img_dir): | |
| ann_list.append(osp.join(gt_dir, img_file.split('.')[0] + '.xml')) | |
| imgs_list.append(osp.join(img_dir, img_file)) | |
| all_files = list(zip(sorted(imgs_list), sorted(ann_list))) | |
| assert len(all_files), f'No images found in {img_dir}' | |
| print(f'Loaded {len(all_files)} images from {img_dir}') | |
| trn_files, val_files = [], [] | |
| if ratio > 0: | |
| for i, file in enumerate(all_files): | |
| if i % math.floor(1 / ratio): | |
| trn_files.append(file) | |
| else: | |
| val_files.append(file) | |
| else: | |
| trn_files, val_files = all_files, [] | |
| print(f'training #{len(trn_files)}, val #{len(val_files)}') | |
| return trn_files, val_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 = mmengine.track_parallel_progress( | |
| load_img_info, files, nproc=nproc) | |
| else: | |
| images = mmengine.track_progress(load_img_info, files) | |
| return images | |
| 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 | |
| assert osp.basename(gt_file).split('.')[0] == osp.basename(img_file).split( | |
| '.')[0] | |
| # read imgs while ignoring orientations | |
| img = mmcv.imread(img_file, 'unchanged') | |
| img_info = dict( | |
| file_name=osp.join(osp.basename(img_file)), | |
| height=img.shape[0], | |
| width=img.shape[1], | |
| segm_file=osp.join(osp.basename(gt_file))) | |
| if osp.splitext(gt_file)[1] == '.xml': | |
| img_info = load_xml_info(gt_file, img_info) | |
| else: | |
| raise NotImplementedError | |
| return img_info | |
| def load_xml_info(gt_file, img_info): | |
| """Collect the annotation information. | |
| Annotation Format | |
| <image> | |
| <imageName>DSC02306.JPG</imageName> | |
| <resolution x="640" y="480" /> | |
| <words> | |
| <word x="61" y="140" width="566" height="107"> | |
| <character x="61" y="147" width="75" height="94" char="C" /> | |
| <character x="173" y="147" width="77" height="93" char="L" /> | |
| <character x="251" y="146" width="83" height="96" char="A" /> | |
| <character x="335" y="146" width="75" height="97" char="V" /> | |
| <character x="409" y="140" width="52" height="105" char="I" /> | |
| <character x="464" y="147" width="76" height="96" char="T" /> | |
| <character x="538" y="154" width="89" height="93" char="A" /> | |
| </word> | |
| </words> | |
| <illumination>no</illumination> | |
| <difficulty>2</difficulty> | |
| <tag> | |
| </tag> | |
| </image> | |
| Args: | |
| gt_file (str): The path to ground-truth | |
| img_info (dict): The dict of the img and annotation information | |
| Returns: | |
| img_info (dict): The dict of the img and annotation information | |
| """ | |
| obj = ET.parse(gt_file) | |
| root = obj.getroot() | |
| anno_info = [] | |
| for word in root.iter('word'): | |
| x, y = max(0, int(word.attrib['x'])), max(0, int(word.attrib['y'])) | |
| w, h = int(word.attrib['width']), int(word.attrib['height']) | |
| bbox = [x, y, w, h] | |
| segmentation = [x, y, x + w, y, x + w, y + h, x, y + h] | |
| anno = dict( | |
| iscrowd=0, | |
| category_id=1, | |
| bbox=bbox, | |
| area=w * h, | |
| segmentation=[segmentation]) | |
| anno_info.append(anno) | |
| img_info.update(anno_info=anno_info) | |
| return img_info | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='Generate training and val set of KAIST ') | |
| parser.add_argument('root_path', help='Root dir path of KAIST') | |
| parser.add_argument( | |
| '--val-ratio', help='Split ratio for val set', default=0.0, type=float) | |
| 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 | |
| ratio = args.val_ratio | |
| trn_files, val_files = collect_files( | |
| osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), ratio) | |
| # Train set | |
| trn_infos = collect_annotations(trn_files, nproc=args.nproc) | |
| with mmengine.Timer( | |
| print_tmpl='It takes {}s to convert KAIST Training annotation'): | |
| dump_ocr_data(trn_infos, osp.join(root_path, | |
| 'instances_training.json'), | |
| 'textdet') | |
| # Val set | |
| if len(val_files) > 0: | |
| val_infos = collect_annotations(val_files, nproc=args.nproc) | |
| with mmengine.Timer( | |
| print_tmpl='It takes {}s to convert KAIST Val annotation'): | |
| dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'), | |
| 'textdet') | |
| if __name__ == '__main__': | |
| main() | |