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[Feature] Add ILST Converter (#833)
* [Feature] Add ILST Converter * [fix] typo * add docs and remove latin * add docs and remove latin * fix bug * fix bugs and docs * fix bugs * add annotation format in load_xml_file and change test_ratio to val_ratio * bug fix * fix docstring * chane _ to - * add ignores to store filtered vertical instances * update doc * update doc * using crops instead of dst_imgs * fix typos and remove test with val * fix docstring * update doc * fix padding size * update doc * simplify bash * update doc * update doc * remove tree * update tree structure * add - before after * add optional * add tab before bash * set val-ratio to 0. * Update docs/en/datasets/det.md * fix lint * fix lint * revert docs Co-authored-by: Tong Gao <gaotongxiao@gmail.com>
This commit is contained in:
Qing Jiang
GitHub
parent
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commit
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205
tools/data/textdet/ilst_converter.py
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205
tools/data/textdet/ilst_converter.py
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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import os.path as osp
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import xml.etree.ElementTree as ET
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import mmcv
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from mmocr.utils import convert_annotations
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def collect_files(img_dir, gt_dir):
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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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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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ann_list, imgs_list = [], []
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for img_file in os.listdir(img_dir):
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ann_path = osp.join(gt_dir, img_file.split('.')[0] + '.xml')
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if os.path.exists(ann_path):
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ann_list.append(ann_path)
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imgs_list.append(osp.join(img_dir, img_file))
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files = list(zip(imgs_list, ann_list))
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assert len(files), f'No images found in {img_dir}'
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print(f'Loaded {len(files)} images from {img_dir}')
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return 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, 'unchanged')
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try:
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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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except AttributeError:
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print(f'Skip broken img {img_file}')
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return None
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if osp.splitext(gt_file)[1] == '.xml':
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img_info = load_xml_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_xml_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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<annotations>
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...
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<object>
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<name>SMT</name>
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<pose>Unspecified</pose>
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<truncated>0</truncated>
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<difficult>0</difficult>
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<bndbox>
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<xmin>157</xmin>
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<ymin>294</ymin>
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<xmax>237</xmax>
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<ymax>357</ymax>
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</bndbox>
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<object>
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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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obj = ET.parse(gt_file)
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root = obj.getroot()
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anno_info = []
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for object in root.iter('object'):
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word = object.find('name').text
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iscrowd = 1 if len(word) == 0 else 0
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x1 = int(object.find('bndbox').find('xmin').text)
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y1 = int(object.find('bndbox').find('ymin').text)
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x2 = int(object.find('bndbox').find('xmax').text)
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y2 = int(object.find('bndbox').find('ymax').text)
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x = max(0, min(x1, x2))
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y = max(0, min(y1, y2))
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w, h = abs(x2 - x1), abs(y2 - y1)
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bbox = [x1, y1, w, h]
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segmentation = [x, y, x + w, y, x + w, y + h, x, y + h]
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anno = dict(
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iscrowd=iscrowd,
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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 split_train_val_list(full_list, val_ratio):
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"""Split list by val_ratio.
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Args:
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full_list (list): List to be splited
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val_ratio (float): Split ratio for val set
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return:
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list(list, list): Train_list and val_list
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"""
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n_total = len(full_list)
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offset = int(n_total * val_ratio)
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if n_total == 0 or offset < 1:
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return [], full_list
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val_list = full_list[:offset]
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train_list = full_list[offset:]
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return [train_list, val_list]
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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 ILST ')
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parser.add_argument('root_path', help='Root dir path of ILST')
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parser.add_argument(
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'--val-ratio', help='Split ratio for val set', default=0., type=float)
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parser.add_argument(
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'--nproc', default=1, type=int, help='Number of processes')
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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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with mmcv.Timer(print_tmpl='It takes {}s to convert ILST annotation'):
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files = collect_files(
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osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'))
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image_infos = collect_annotations(files, nproc=args.nproc)
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if args.val_ratio:
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image_infos = split_train_val_list(image_infos, args.val_ratio)
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splits = ['training', 'val']
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else:
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image_infos = [image_infos]
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splits = ['training']
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for i, split in enumerate(splits):
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convert_annotations(
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list(filter(None, image_infos[i])),
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osp.join(root_path, 'instances_' + split + '.json'))
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if __name__ == '__main__':
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main()
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270
tools/data/textrecog/ilst_converter.py
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270
tools/data/textrecog/ilst_converter.py
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import json
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import os
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import os.path as osp
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import xml.etree.ElementTree as ET
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import mmcv
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from mmocr.datasets.pipelines.crop import crop_img
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from mmocr.utils.fileio import list_to_file
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def collect_files(img_dir, gt_dir):
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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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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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ann_list, imgs_list = [], []
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for img_file in os.listdir(img_dir):
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ann_path = osp.join(gt_dir, img_file.split('.')[0] + '.xml')
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if os.path.exists(ann_path):
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ann_list.append(ann_path)
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imgs_list.append(osp.join(img_dir, img_file))
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files = list(zip(imgs_list, ann_list))
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assert len(files), f'No images found in {img_dir}'
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print(f'Loaded {len(files)} images from {img_dir}')
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return 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, 'unchanged')
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try:
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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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except AttributeError:
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print(f'Skip broken img {img_file}')
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return None
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if osp.splitext(gt_file)[1] == '.xml':
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img_info = load_xml_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_xml_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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<annotations>
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...
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<object>
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<name>SMT</name>
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<pose>Unspecified</pose>
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<truncated>0</truncated>
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<difficult>0</difficult>
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<bndbox>
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<xmin>157</xmin>
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<ymin>294</ymin>
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<xmax>237</xmax>
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<ymax>357</ymax>
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</bndbox>
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<object>
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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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obj = ET.parse(gt_file)
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root = obj.getroot()
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anno_info = []
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for object in root.iter('object'):
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word = object.find('name').text
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x1 = int(object.find('bndbox').find('xmin').text)
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y1 = int(object.find('bndbox').find('ymin').text)
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x2 = int(object.find('bndbox').find('xmax').text)
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y2 = int(object.find('bndbox').find('ymax').text)
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x = max(0, min(x1, x2))
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y = max(0, min(y1, y2))
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w, h = abs(x2 - x1), abs(y2 - y1)
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bbox = [x, y, x + w, y, x + w, y + h, x, y + h]
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anno = dict(bbox=bbox, word=word)
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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 split_train_val_list(full_list, val_ratio):
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"""Split list by val_ratio.
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Args:
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full_list (list): List to be splited
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val_ratio (float): Split ratio for val set
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return:
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list(list, list): Train_list and val_list
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"""
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n_total = len(full_list)
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offset = int(n_total * val_ratio)
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if n_total == 0 or offset < 1:
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return [], full_list
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val_list = full_list[:offset]
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train_list = full_list[offset:]
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return [train_list, val_list]
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def generate_ann(root_path, image_infos, preserve_vertical, val_ratio, format):
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"""Generate cropped annotations and label txt file.
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Args:
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root_path (str): The root path of the dataset
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split (str): The split of dataset. Namely: training or test
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image_infos (list[dict]): A list of dicts of the img and
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annotation information
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preserve_vertical (bool): Whether to preserve vertical texts
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val_ratio (float): Split ratio for val set
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format (str): Using jsonl(dict) or str to format annotations
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"""
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assert val_ratio <= 1.
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if val_ratio:
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image_infos = split_train_val_list(image_infos, val_ratio)
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splits = ['training', 'val']
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else:
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image_infos = [image_infos]
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splits = ['training']
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for i, split in enumerate(splits):
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dst_image_root = osp.join(root_path, 'crops', split)
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ignore_image_root = osp.join(root_path, 'ignores', split)
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dst_label_file = osp.join(root_path, f'{split}_label.{format}')
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os.makedirs(dst_image_root, exist_ok=True)
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lines = []
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for image_info in image_infos[i]:
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index = 1
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src_img_path = osp.join(root_path, 'imgs', image_info['file_name'])
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image = mmcv.imread(src_img_path)
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src_img_root = image_info['file_name'].split('.')[0]
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for anno in image_info['anno_info']:
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word = anno['word']
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dst_img = crop_img(image, anno['bbox'], 0, 0)
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h, w, _ = dst_img.shape
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dst_img_name = f'{src_img_root}_{index}.png'
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index += 1
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# Skip invalid annotations
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if min(dst_img.shape) == 0:
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continue
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# Skip vertical texts
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if not preserve_vertical and h / w > 2 and split == 'training':
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dst_img_path = osp.join(ignore_image_root, dst_img_name)
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else:
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dst_img_path = osp.join(dst_image_root, dst_img_name)
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mmcv.imwrite(dst_img, dst_img_path)
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filename = f'{osp.basename(dst_image_root)}/{dst_img_name}'
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if format == 'txt':
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lines.append(f'{filename} ' f'{word}')
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elif format == 'jsonl':
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lines.append(
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json.dumps({
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'filename': filename,
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'text': word
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},
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ensure_ascii=False))
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else:
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raise NotImplementedError
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list_to_file(dst_label_file, lines)
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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 ILST ')
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parser.add_argument('root_path', help='Root dir path of ILST')
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parser.add_argument(
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'--preserve-vertical',
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help='Preserve samples containing vertical texts',
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action='store_true')
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parser.add_argument(
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'--val-ratio', help='Split ratio for val set', default=0., type=float)
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parser.add_argument(
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'--nproc', default=1, type=int, help='Number of processes')
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parser.add_argument(
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'--format',
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default='jsonl',
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help='Use jsonl or string to format annotations',
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choices=['jsonl', 'txt'])
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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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with mmcv.Timer(print_tmpl='It takes {}s to convert ILST annotation'):
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files = collect_files(
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osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'))
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image_infos = collect_annotations(files, nproc=args.nproc)
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# filter broken images
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image_infos = list(filter(None, image_infos))
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generate_ann(root_path, image_infos, args.preserve_vertical,
|
||||
args.val_ratio, args.format)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
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Reference in New Issue
Block a user