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[Feature] Add CurvedSyntext150k Converter (#719)
* [Feature] Add bezier_to_polygon to box_util * Add num_sample to parameter * add sort_point util * update docstring * Add curvedsyntext converter
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Tong Gao
GitHub
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@ -32,6 +32,10 @@ The structure of the text detection dataset directory is organized as follows.
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│ ├── imgs
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│ ├── instances_test.json
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│ └── instances_training.json
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├── CurvedSynText150k
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│ ├── syntext_word_eng
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│ ├── emcs_imgs
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│ └── instances_training.json
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```
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|Dataset|Images| | Annotation Files | | |
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@ -43,6 +47,7 @@ The structure of the text detection dataset directory is organized as follows.
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| Synthtext | [homepage](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) | instances_training.lmdb ([data.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/data.mdb), [lock.mdb](https://download.openmmlab.com/mmocr/data/synthtext/instances_training.lmdb/lock.mdb)) | - | - |
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| TextOCR | [homepage](https://textvqa.org/textocr/dataset) | - | - | -
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| Totaltext | [homepage](https://github.com/cs-chan/Total-Text-Dataset) | - | - | -
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| CurvedSynText150k | [homepage](https://github.com/aim-uofa/AdelaiDet/blob/master/datasets/README.md) \| [Part1](https://drive.google.com/file/d/1OSJ-zId2h3t_-I7g_wUkrK-VqQy153Kj/view?usp=sharing) \| [Part2](https://drive.google.com/file/d/1EzkcOlIgEp5wmEubvHb7-J5EImHExYgY/view?usp=sharing) | [instances_training.json](https://download.openmmlab.com/mmocr/data/curvedsyntext/instances_training.json) | - | - |
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## Important Note
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@ -149,3 +154,27 @@ mv Polygon/Test ../annotations/test
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```bash
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python tools/data/textdet/totaltext_converter.py /path/to/totaltext -o /path/to/totaltext --split-list training test
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```
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### CurvedSynText150k
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- Step1: Download [syntext1.zip](https://drive.google.com/file/d/1OSJ-zId2h3t_-I7g_wUkrK-VqQy153Kj/view?usp=sharing) and [syntext2.zip](https://drive.google.com/file/d/1EzkcOlIgEp5wmEubvHb7-J5EImHExYgY/view?usp=sharing) to `CurvedSynText150k/`.
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- Step2:
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```bash
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unzip -q syntext1.zip
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mv train.json train1.json
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unzip images.zip
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rm images.zip
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unzip -q syntext2.zip
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mv train.json train2.json
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unzip images.zip
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rm images.zip
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```
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- Step3: Download [instances_training.json](https://download.openmmlab.com/mmocr/data/curvedsyntext/instances_training.json) to `CurvedSynText150k/`
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- Or, generate `instances_training.json` with following command:
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```bash
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python tools/data/common/curvedsyntext_converter.py PATH/TO/CurvedSynText150k --nproc 4
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```
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129
tools/data/common/curvedsyntext_converter.py
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129
tools/data/common/curvedsyntext_converter.py
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@ -0,0 +1,129 @@
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# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os.path as osp
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from functools import partial
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import mmcv
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import numpy as np
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from mmocr.utils import bezier_to_polygon, sort_points
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# The default dictionary used by CurvedSynthText
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dict95 = [
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' ', '!', '"', '#', '$', '%', '&', '\'', '(', ')', '*', '+', ',', '-', '.',
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'/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<', '=',
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'>', '?', '@', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L',
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'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '[',
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'\\', ']', '^', '_', '`', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j',
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'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y',
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'z', '{', '|', '}', '~'
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]
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UNK = len(dict95)
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EOS = UNK + 1
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def digit2text(rec):
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res = []
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for d in rec:
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assert d <= EOS
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if d == EOS:
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break
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if d == UNK:
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print('Warning: Has a UNK character')
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res.append('口') # Or any special character not in the target dict
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res.append(dict95[d])
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return ''.join(res)
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def modify_annotation(ann, num_sample, start_img_id=0, start_ann_id=0):
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ann['text'] = digit2text(ann.pop('rec'))
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# Get hide egmentation points
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polygon_pts = bezier_to_polygon(ann['bezier_pts'], num_sample=num_sample)
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ann['segmentation'] = np.asarray(sort_points(polygon_pts)).reshape(
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1, -1).tolist()
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ann['image_id'] += start_img_id
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ann['id'] += start_ann_id
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return ann
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def modify_image_info(image_info, path_prefix, start_img_id=0):
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image_info['file_name'] = osp.join(path_prefix, image_info['file_name'])
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image_info['id'] += start_img_id
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return image_info
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Convert CurvedSynText150k to COCO format')
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parser.add_argument('root_path', help='CurvedSynText150k root path')
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parser.add_argument('-o', '--out-dir', help='Output path')
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parser.add_argument(
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'-n',
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'--num-sample',
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type=int,
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default=4,
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help='Number of sample points at each Bezier curve.')
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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 convert_annotations(data,
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path_prefix,
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num_sample,
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nproc,
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start_img_id=0,
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start_ann_id=0):
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modify_image_info_with_params = partial(
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modify_image_info, path_prefix=path_prefix, start_img_id=start_img_id)
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modify_annotation_with_params = partial(
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modify_annotation,
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num_sample=num_sample,
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start_img_id=start_img_id,
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start_ann_id=start_ann_id)
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if nproc > 1:
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data['annotations'] = mmcv.track_parallel_progress(
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modify_annotation_with_params, data['annotations'], nproc=nproc)
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data['images'] = mmcv.track_parallel_progress(
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modify_image_info_with_params, data['images'], nproc=nproc)
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else:
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data['annotations'] = mmcv.track_progress(
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modify_annotation_with_params, data['annotations'])
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data['images'] = mmcv.track_progress(
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modify_image_info_with_params,
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data['images'],
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)
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data['categories'] = [{'id': 1, 'name': 'text'}]
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return data
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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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out_dir = args.out_dir if args.out_dir else root_path
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mmcv.mkdir_or_exist(out_dir)
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anns = mmcv.load(osp.join(root_path, 'train1.json'))
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data1 = convert_annotations(anns, 'syntext_word_eng', args.num_sample,
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args.nproc)
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# Get the maximum image id from data1
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start_img_id = max(data1['images'], key=lambda x: x['id'])['id'] + 1
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start_ann_id = max(data1['annotations'], key=lambda x: x['id'])['id'] + 1
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anns = mmcv.load(osp.join(root_path, 'train2.json'))
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data2 = convert_annotations(
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anns,
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'emcs_imgs',
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args.num_sample,
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args.nproc,
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start_img_id=start_img_id,
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start_ann_id=start_ann_id)
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data1['images'] += data2['images']
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data1['annotations'] += data2['annotations']
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mmcv.dump(data1, osp.join(out_dir, 'instances_training.json'))
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if __name__ == '__main__':
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main()
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