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[Feature] Add FUNSD Converter (#808) 

* Add FUNSD Converter

* Update tools/data/textrecog/funsd_converter.py

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

* Update tools/data/textrecog/funsd_converter.py

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

* Update tools/data/textdet/funsd_converter.py

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

* blank line between sections

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

* fix incorrect docstrings

* fix docstrings & fix timer

* add --preserve-vertical arg for preserving vertical texts

* fix --preserve-vertical

* [doc] fix recog.md incorrect description

* fix docstring style

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

* fix docstring spaces

Co-authored-by: Tong Gao <gaotongxiao@gmail.com>
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54
docs/en/datasets/det.md
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@ -36,18 +36,25 @@ The structure of the text detection dataset directory is organized as follows.
│   ├── syntext_word_eng
│   ├── emcs_imgs
│   └── instances_training.json
|── funsd
|   ├── annotations
│   ├── imgs
│   ├── instances_test.json
│   └── instances_training.json
```
|Dataset|Images| | Annotation Files | | |
| :-------: | :------------------------------------------------------------: | :----------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :-------------------------------------: | :--------------------------------------------------------------------------------------------: |
| | | training | validation | testing | |
| CTW1500 | [homepage](https://github.com/Yuliang-Liu/Curve-Text-Detector) | - | - | - |
| ICDAR2015 | [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads) | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json) | - | [instances_test.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json) |
| ICDAR2017 | [homepage](https://rrc.cvc.uab.es/?ch=8&com=downloads) | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_training.json) | [instances_val.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_val.json) | - | | |
| 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)) | - | - |
| TextOCR | [homepage](https://textvqa.org/textocr/dataset) | - | - | -
| 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) | - | - |
| Dataset | Images | | Annotation Files | | |
| :---------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | :---: |
| | | training | validation | testing | |
| CTW1500 | [homepage](https://github.com/Yuliang-Liu/Curve-Text-Detector) | - | - | - |
| ICDAR2015 | [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads) | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_training.json) | - | [instances_test.json](https://download.openmmlab.com/mmocr/data/icdar2015/instances_test.json) |
| ICDAR2017 | [homepage](https://rrc.cvc.uab.es/?ch=8&com=downloads) | [instances_training.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_training.json) | [instances_val.json](https://download.openmmlab.com/mmocr/data/icdar2017/instances_val.json) | - | | |
| 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)) | - | - |
| TextOCR | [homepage](https://textvqa.org/textocr/dataset) | - | - | - |
| 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/) | - | - | - |
## Important Note
@ -178,3 +185,30 @@ rm images.zip
```bash
python tools/data/common/curvedsyntext_converter.py PATH/TO/CurvedSynText150k --nproc 4
```
### FUNSD
- Step1: Download [dataset.zip](https://guillaumejaume.github.io/FUNSD/dataset.zip) to `funsd/`.
```bash
mkdir funsd && cd funsd
# Download FUNSD dataset
wget https://guillaumejaume.github.io/FUNSD/dataset.zip
unzip -q dataset.zip
# For images
mv dataset/training_data/images imgs && mv dataset/testing_data/images/* imgs/
# For annotations
mkdir annotations
mv dataset/training_data/annotations annotations/training && mv dataset/testing_data/annotations annotations/test
rm dataset.zip && rm -rf dataset
```
- Step2: Generate `instances_training.json` and `instances_test.json` with following command:
```bash
python tools/data/textdet/funsd_converter.py PATH/TO/funsd --nproc 4
```

69
docs/en/datasets/recog.md
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@ -73,25 +73,33 @@
│ │ ├── train_5_label.txt
│ │ ├── train_f_label.txt
│ │ ├── val_label.txt
│   ├── funsd
│ │ ├── imgs
│ │ ├── dst_imgs
│ │ ├── annotations
│ │ ├── train_label.txt
│ │ ├── test_label.txt
```
| Dataset | images | annotation file | annotation file |
| :--------: | :-----------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------: |
| | | training | test |
| coco_text | [homepage](https://rrc.cvc.uab.es/?ch=5&com=downloads) | [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/coco_text/train_label.txt) | - | |
| icdar_2011 | [homepage](http://www.cvc.uab.es/icdar2011competition/?com=downloads) | [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/train_label.txt) | - | |
| icdar_2013 | [homepage](https://rrc.cvc.uab.es/?ch=2&com=downloads) | [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/train_label.txt) | [test_label_1015.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/test_label_1015.txt) | |
| icdar_2015 | [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads) | [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/train_label.txt) | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/test_label.txt) | |
| IIIT5K | [homepage](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html) | [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/train_label.txt) | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/test_label.txt) | |
| ct80 | [homepage](http://cs-chan.com/downloads_CUTE80_dataset.html) | - | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/ct80/test_label.txt) | |
| svt |[homepage](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset) | - | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/svt/test_label.txt) | |
| svtp | [unofficial homepage\[1\]](https://github.com/Jyouhou/Case-Sensitive-Scene-Text-Recognition-Datasets) | - | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/svtp/test_label.txt) | |
| MJSynth (Syn90k) | [homepage](https://www.robots.ox.ac.uk/~vgg/data/text/) | [shuffle_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/shuffle_labels.txt) \| [label.txt](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/label.txt) | - | |
| SynthText (Synth800k) | [homepage](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) | [alphanumeric_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/alphanumeric_labels.txt) \|[shuffle_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/shuffle_labels.txt) \| [instances_train.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/instances_train.txt) \| [label.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/label.txt) | - | |
| SynthAdd | [SynthText_Add.zip](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg) (code:627x) | [label.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthAdd/label.txt) | - | |
| TextOCR | [homepage](https://textvqa.org/textocr/dataset) | - | - | |
| 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)| |
| Dataset | images | annotation file | annotation file |
| :-------------------: | :---------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------: |
| | | training | test |
| coco_text | [homepage](https://rrc.cvc.uab.es/?ch=5&com=downloads) | [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/coco_text/train_label.txt) | - | |
| icdar_2011 | [homepage](http://www.cvc.uab.es/icdar2011competition/?com=downloads) | [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/train_label.txt) | - | |
| icdar_2013 | [homepage](https://rrc.cvc.uab.es/?ch=2&com=downloads) | [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/train_label.txt) | [test_label_1015.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2013/test_label_1015.txt) | |
| icdar_2015 | [homepage](https://rrc.cvc.uab.es/?ch=4&com=downloads) | [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/train_label.txt) | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/icdar_2015/test_label.txt) | |
| IIIT5K | [homepage](http://cvit.iiit.ac.in/projects/SceneTextUnderstanding/IIIT5K.html) | [train_label.txt](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/train_label.txt) | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/IIIT5K/test_label.txt) | |
| ct80 | [homepage](http://cs-chan.com/downloads_CUTE80_dataset.html) | - | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/ct80/test_label.txt) | |
| svt | [homepage](http://www.iapr-tc11.org/mediawiki/index.php/The_Street_View_Text_Dataset) | - | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/svt/test_label.txt) | |
| svtp | [unofficial homepage\[1\]](https://github.com/Jyouhou/Case-Sensitive-Scene-Text-Recognition-Datasets) | - | [test_label.txt](https://download.openmmlab.com/mmocr/data/mixture/svtp/test_label.txt) | |
| MJSynth (Syn90k) | [homepage](https://www.robots.ox.ac.uk/~vgg/data/text/) | [shuffle_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/shuffle_labels.txt) \| [label.txt](https://download.openmmlab.com/mmocr/data/mixture/Syn90k/label.txt) | - | |
| SynthText (Synth800k) | [homepage](https://www.robots.ox.ac.uk/~vgg/data/scenetext/) | [alphanumeric_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/alphanumeric_labels.txt) \|[shuffle_labels.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/shuffle_labels.txt) \| [instances_train.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/instances_train.txt) \| [label.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthText/label.txt) | - | |
| SynthAdd | [SynthText_Add.zip](https://pan.baidu.com/s/1uV0LtoNmcxbO-0YA7Ch4dg) (code:627x) | [label.txt](https://download.openmmlab.com/mmocr/data/mixture/SynthAdd/label.txt) | - | |
| TextOCR | [homepage](https://textvqa.org/textocr/dataset) | - | - | |
| 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/) | - | - | |
(*) Since the official homepage is unavailable now, we provide an alternative for quick reference. However, we do not guarantee the correctness of the dataset.
@ -286,3 +294,30 @@ python tools/data/utils/txt2lmdb.py -i data/mixture/Syn90k/label.txt -o data/mix
```bash
python tools/data/textrecog/openvino_converter.py /path/to/openvino 4
```
### FUNSD
- Step1: Download [dataset.zip](https://guillaumejaume.github.io/FUNSD/dataset.zip) to `funsd/`.
```bash
mkdir funsd && cd funsd
# Download FUNSD dataset
wget https://guillaumejaume.github.io/FUNSD/dataset.zip
unzip -q dataset.zip
# For images
mv dataset/training_data/images imgs && mv dataset/testing_data/images/* imgs/
# For annotations
mkdir annotations
mv dataset/training_data/annotations annotations/training && mv dataset/testing_data/annotations annotations/test
rm dataset.zip && rm -rf dataset
```
- Step2: Generate `train_label.txt` and `test_label.txt` and crop images using 4 processes with following command (add `--preserve-vertical` if you wish to preserve the images containing vertical texts):
```bash
python tools/data/textrecog/funsd_converter.py PATH/TO/funsd --nproc 4
```

157
tools/data/textdet/funsd_converter.py 100644
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@ -0,0 +1,157 @@
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import math
import os
import os.path as osp
import mmcv
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 gt_file in os.listdir(gt_dir):
ann_list.append(osp.join(gt_dir, gt_file))
imgs_list.append(osp.join(img_dir, gt_file.replace('.json', '.png')))
files = list(zip(sorted(imgs_list), sorted(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
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] == '.json':
img_info = load_json_info(gt_file, img_info)
else:
raise NotImplementedError
return img_info
def load_json_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
"""
annotation = mmcv.load(gt_file)
anno_info = []
for form in annotation['form']:
for ann in form['words']:
iscrowd = 1 if len(ann['text']) == 0 else 0
x1, y1, x2, y2 = ann['box']
x = max(0, min(math.floor(x1), math.floor(x2)))
y = max(0, min(math.floor(y1), math.floor(y2)))
w, h = math.ceil(abs(x2 - x1)), math.ceil(abs(y2 - y1))
bbox = [x, y, w, h]
segmentation = [x, y, x + w, y, x + w, y + h, x, y + h]
anno = dict(
iscrowd=iscrowd,
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 test set of FUNSD ')
parser.add_argument('root_path', help='Root dir path of FUNSD')
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', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert FUNSD annotation'):
files = collect_files(
osp.join(root_path, 'imgs'),
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()

203
tools/data/textrecog/funsd_converter.py 100644
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@ -0,0 +1,203 @@
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import math
import os
import os.path as osp
import mmcv
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 gt_file in os.listdir(gt_dir):
ann_list.append(osp.join(gt_dir, gt_file))
imgs_list.append(osp.join(img_dir, gt_file.replace('.json', '.png')))
files = list(zip(sorted(imgs_list), sorted(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
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] == '.json':
img_info = load_json_info(gt_file, img_info)
else:
raise NotImplementedError
return img_info
def load_json_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
"""
annotation = mmcv.load(gt_file)
anno_info = []
for form in annotation['form']:
for ann in form['words']:
# Ignore illegible samples
if len(ann['text']) == 0:
continue
x1, y1, x2, y2 = ann['box']
x = max(0, min(math.floor(x1), math.floor(x2)))
y = max(0, min(math.floor(y1), math.floor(y2)))
w, h = math.ceil(abs(x2 - x1)), math.ceil(abs(y2 - y1))
bbox = [x, y, x + w, y, x + w, y + h, x, y + h]
word = ann['text']
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):
"""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
"""
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 = image_info['file_name'].split('.')[0]
for anno in image_info['anno_info']:
word = anno['word']
dst_img = crop_img(image, anno['bbox'])
h, w, _ = dst_img.shape
# Skip invalid annotations
if min(dst_img.shape) == 0:
continue
# Skip vertical texts
if not preserve_vertical and h / w > 2:
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 parse_args():
parser = argparse.ArgumentParser(
description='Generate training and test set of FUNSD ')
parser.add_argument('root_path', help='Root dir path of FUNSD')
parser.add_argument(
'--preserve-vertical',
help='Preserve samples containing vertical texts',
action='store_true')
parser.add_argument(
'--nproc', default=1, type=int, help='Number of processes')
args = parser.parse_args()
return args
def main():
args = parse_args()
root_path = args.root_path
for split in ['training', 'test']:
print(f'Processing {split} set...')
with mmcv.Timer(print_tmpl='It takes {}s to convert FUNSD annotation'):
files = collect_files(
osp.join(root_path, 'imgs'),
osp.join(root_path, 'annotations', split))
image_infos = collect_annotations(files, nproc=args.nproc)
generate_ann(root_path, split, image_infos, args.preserve_vertical)
if __name__ == '__main__':
main()