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[Feature] Add DeText Converter (#818)

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* add DeText Converter

* Update tools/data/textrecog/detext_converter.py

Co-authored-by: Tong Gao <gaotongxiao@gmail.com>

* update doc; support jsonl; fix docstrings

* update mkdir func

* fix bug

* update doc; do not filter for test val

* move directory tree

* fix indentation

Co-authored-by: Tong Gao <gaotongxiao@gmail.com>
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Xinyu Wang 2022-03-30 14:43:33 +08:00 committed by GitHub
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38
docs/en/datasets/det.md
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@ -59,6 +59,7 @@ The structure of the text detection dataset directory is organized as follows.
| Totaltext | [homepage](https://github.com/cs-chan/Total-Text-Dataset) | - | - | - |
| 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) | - | - |
| FUNSD | [homepage](https://guillaumejaume.github.io/FUNSD/) | - | - | - |
| DeText | [homepage](https://rrc.cvc.uab.es/?ch=9) | - | - | - |
| NAF | [homepage](https://github.com/herobd/NAF_dataset/releases/tag/v1.0) | - | - | - |
| SROIE | [homepage](https://rrc.cvc.uab.es/?ch=13) | - | - | - |
| Lecture Video DB | [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb) | - | - | - |
@ -221,6 +222,43 @@ rm dataset.zip && rm -rf dataset
python tools/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
```
### DeText
- Step1: Download `ch9_training_images.zip`, `ch9_training_localization_transcription_gt.zip`, `ch9_validation_images.zip`, and `ch9_validation_localization_transcription_gt.zip` from **Task 3: End to End** on the [homepage](https://rrc.cvc.uab.es/?ch=9).
```bash
mkdir detext && cd detext
mkdir imgs && mkdir annotations && mkdir imgs/training && mkdir imgs/val && mkdir annotations/training && mkdir annotations/val
# Download DeText
wget https://rrc.cvc.uab.es/downloads/ch9_training_images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch9_training_localization_transcription_gt.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch9_validation_images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch9_validation_localization_transcription_gt.zip --no-check-certificate
# Extract images and annotations
unzip -q ch9_training_images.zip -d imgs/training && unzip -q ch9_training_localization_transcription_gt.zip -d annotations/training && unzip -q ch9_validation_images.zip -d imgs/val && unzip -q ch9_validation_localization_transcription_gt.zip -d annotations/val
# Remove zips
rm ch9_training_images.zip && rm ch9_training_localization_transcription_gt.zip && rm ch9_validation_images.zip && rm ch9_validation_localization_transcription_gt.zip
```
- Step2: Generate `instances_training.json` and `instances_val.json` with following command:
```bash
python tools/data/textdet/detext_converter.py PATH/TO/detext --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
|── detext
|   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
```
### NAF
- Step1: Download [labeled_images.tar.gz](https://github.com/herobd/NAF_dataset/releases/tag/v1.0) to `naf/`.

40
docs/en/datasets/recog.md
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@ -103,6 +103,7 @@
| Totaltext | [homepage](https://github.com/cs-chan/Total-Text-Dataset) | - | - | |
| OpenVINO | [Open Images](https://github.com/cvdfoundation/open-images-dataset) | [annotations](https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text) | [annotations](https://storage.openvinotoolkit.org/repositories/openvino_training_extensions/datasets/open_images_v5_text) | |
| FUNSD | [homepage](https://guillaumejaume.github.io/FUNSD/) | - | - | |
| DeText | [homepage](https://rrc.cvc.uab.es/?ch=9) | - | - | |
| NAF | [homepage](https://github.com/herobd/NAF_dataset) | - | - | - |
| SROIE | [homepage](https://rrc.cvc.uab.es/?ch=13) | - | - | - |
| Lecture Video DB | [homepage](https://cvit.iiit.ac.in/research/projects/cvit-projects/lecturevideodb) | - | - | - |
@ -329,6 +330,45 @@ rm dataset.zip && rm -rf dataset
python tools/data/textrecog/funsd_converter.py PATH/TO/funsd --nproc 4
```
### DeText
- Step1: Download `ch9_training_images.zip`, `ch9_training_localization_transcription_gt.zip`, `ch9_validation_images.zip`, and `ch9_validation_localization_transcription_gt.zip` from **Task 3: End to End** on the [homepage](https://rrc.cvc.uab.es/?ch=9).
```bash
mkdir detext && cd detext
mkdir imgs && mkdir annotations && mkdir imgs/training && mkdir imgs/val && mkdir annotations/training && mkdir annotations/val
# Download DeText
wget https://rrc.cvc.uab.es/downloads/ch9_training_images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch9_training_localization_transcription_gt.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch9_validation_images.zip --no-check-certificate
wget https://rrc.cvc.uab.es/downloads/ch9_validation_localization_transcription_gt.zip --no-check-certificate
# Extract images and annotations
unzip -q ch9_training_images.zip -d imgs/training && unzip -q ch9_training_localization_transcription_gt.zip -d annotations/training && unzip -q ch9_validation_images.zip -d imgs/val && unzip -q ch9_validation_localization_transcription_gt.zip -d annotations/val
# Remove zips
rm ch9_training_images.zip && rm ch9_training_localization_transcription_gt.zip && rm ch9_validation_images.zip && rm ch9_validation_localization_transcription_gt.zip
```
- Step2: Generate `instances_training.json` and `instances_val.json` with following command:
```bash
# Add --preserve-vertical to preserve vertical texts for training, otherwise
# vertical images will be filtered and stored in PATH/TO/detext/ignores
python tools/data/textrecog/detext_converter.py PATH/TO/detext --nproc 4
```
- After running the above codes, the directory structure should be as follows:
```text
├── detext
│ ├── crops
│ ├── ignores
│ ├── train_label.jsonl
│ ├── test_label.jsonl
```
### NAF
- Step1: Download [labeled_images.tar.gz](https://github.com/herobd/NAF_dataset/releases/tag/v1.0) to `naf/`.

161
tools/data/textdet/detext_converter.py Normal file
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@ -0,0 +1,161 @@
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import mmcv
import numpy as np
from mmocr.utils import convert_annotations
def collect_files(img_dir, gt_dir):
"""Collect all images and their corresponding groundtruth files.
Args:
img_dir (str): The image directory
gt_dir (str): The groundtruth directory
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
ann_list, imgs_list = [], []
for img in os.listdir(img_dir):
imgs_list.append(osp.join(img_dir, img))
ann_list.append(osp.join(gt_dir, 'gt_' + img.replace('jpg', 'txt')))
files = list(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 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 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] == '.txt':
img_info = load_txt_info(gt_file, img_info)
else:
raise NotImplementedError
return img_info
def load_txt_info(gt_file, img_info):
"""Collect the annotation information.
# Annotation Format
# x1, y1, x2, y2, x3, y3, x4, y4, transcript
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
"""
with open(gt_file, 'r') as f:
anno_info = []
annotations = f.readlines()
for ann in annotations:
try:
ann_box = np.array(ann.split(',')[0:8]).astype(int).tolist()
except ValueError:
# skip invalid annotation line
continue
x = max(0, min(ann_box[0::2]))
y = max(0, min(ann_box[1::2]))
w, h = max(ann_box[0::2]) - x, max(ann_box[1::2]) - y
bbox = [x, y, w, h]
segmentation = ann_box
word = ann.split(',')[-1].replace('\n', '').strip()
anno = dict(
iscrowd=0 if word != '###' else 1,
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 DeText ')
parser.add_argument('root_path', help='Root dir path of DeText')
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
for split in ['training', 'val']:
print(f'Processing {split} set...')
with mmcv.Timer(
print_tmpl='It takes {}s to convert DeText annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),
osp.join(root_path, 'annotations', split))
image_infos = collect_annotations(files, nproc=args.nproc)
convert_annotations(
image_infos, osp.join(root_path,
'instances_' + split + '.json'))
if __name__ == '__main__':
main()

224
tools/data/textrecog/detext_converter.py Normal file
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@ -0,0 +1,224 @@
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import json
import os
import os.path as osp
import mmcv
import numpy as np
from mmocr.datasets.pipelines.crop import crop_img
from mmocr.utils.fileio import list_to_file
def collect_files(img_dir, gt_dir):
"""Collect all images and their corresponding groundtruth files.
Args:
img_dir (str): The image directory
gt_dir (str): The groundtruth directory
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
ann_list, imgs_list = [], []
for img in os.listdir(img_dir):
imgs_list.append(osp.join(img_dir, img))
ann_list.append(osp.join(gt_dir, 'gt_' + img.replace('jpg', 'txt')))
files = list(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 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 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] == '.txt':
img_info = load_txt_info(gt_file, img_info)
else:
raise NotImplementedError
return img_info
def load_txt_info(gt_file, img_info):
"""Collect the annotation information.
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
"""
with open(gt_file, 'r') as f:
anno_info = []
annotations = f.readlines()
for ann in annotations:
# Annotation format [x1, y1, x2, y2, x3, y3, x4, y4, transcript]
try:
bbox = np.array(ann.split(',')[0:8]).astype(int).tolist()
except ValueError:
# Skip invalid annotation line
continue
word = ann.split(',')[-1].replace('\n', '').strip()
# Skip samples without recog gt
if word == '###':
continue
anno = dict(bbox=bbox, word=word)
anno_info.append(anno)
img_info.update(anno_info=anno_info)
return img_info
def generate_ann(root_path, split, image_infos, preserve_vertical, format):
"""Generate cropped annotations and label txt file.
Args:
root_path (str): The root path of the dataset
split (str): The split of dataset. Namely: training or test
image_infos (list[dict]): A list of dicts of the img and
annotation information
preserve_vertical (bool): Whether to preserve vertical texts
format (str): Annotation format, should be either 'txt' or 'jsonl'
"""
dst_image_root = osp.join(root_path, 'crops', split)
ignore_image_root = osp.join(root_path, 'ignores', split)
if split == 'training':
dst_label_file = osp.join(root_path, f'train_label.{format}')
elif split == 'val':
dst_label_file = osp.join(root_path, f'val_label.{format}')
mmcv.mkdir_or_exist(dst_image_root)
mmcv.mkdir_or_exist(ignore_image_root)
lines = []
for image_info in image_infos:
index = 1
src_img_path = osp.join(root_path, 'imgs', split,
image_info['file_name'])
image = mmcv.imread(src_img_path)
src_img_root = image_info['file_name'].split('.')[0]
for anno in image_info['anno_info']:
word = anno['word']
dst_img = crop_img(image, anno['bbox'], 0, 0)
h, w, _ = dst_img.shape
dst_img_name = f'{src_img_root}_{index}.png'
index += 1
# Skip invalid annotations
if min(dst_img.shape) == 0 or len(word) == 0:
continue
# Filter out vertical texts
if not preserve_vertical and h / w > 2 and split == 'training':
dst_img_path = osp.join(ignore_image_root, dst_img_name)
else:
dst_img_path = osp.join(dst_image_root, dst_img_name)
mmcv.imwrite(dst_img, dst_img_path)
if format == 'txt':
lines.append(f'{osp.basename(dst_image_root)}/{dst_img_name} '
f'{word}')
elif format == 'jsonl':
lines.append(
json.dumps({
'filename':
f'{osp.basename(dst_image_root)}/{dst_img_name}',
'text': word
}))
else:
raise NotImplementedError
list_to_file(dst_label_file, lines)
def parse_args():
parser = argparse.ArgumentParser(
description='Generate training and val set of DeText ')
parser.add_argument('root_path', help='Root dir path of DeText')
parser.add_argument(
'--preserve-vertical',
help='Preserve samples containing vertical texts',
action='store_true')
parser.add_argument(
'--format',
default='jsonl',
help='Use jsonl or string to format annotations',
choices=['jsonl', 'txt'])
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
for split in ['training', 'val']:
print(f'Processing {split} set...')
with mmcv.Timer(
print_tmpl='It takes {}s to convert DeText annotation'):
files = collect_files(
osp.join(root_path, 'imgs', split),
osp.join(root_path, 'annotations', split))
image_infos = collect_annotations(files, nproc=args.nproc)
generate_ann(root_path, split, image_infos, args.preserve_vertical,
args.format)
if __name__ == '__main__':
main()