A super-resolution dataset consists of paired LR-HR scene text images.
Requirements
- 1.0.1 <= torch < 1.6.0
- torchvision -c pytorch
- lmdb >= 0.98
- pillow >= 7.1.2
- numpy
- six
- tqdm
- python-opencv
- easydict
- yaml
Train and Test
download the Aster model from https://github.com/ayumiymk/aster.pytorch, Moran model from https://github.com/Canjie-Luo/MORAN_v2, CRNN model from https://github.com/meijieru/crnn.pytorch.
Change TRAIN.VAL.rec_pretrained in src/configs/super_resolution.yaml to your Aster model path, change TRAIN.VAL.moran_pretrained to your MORAN model path and
change TRAIN.VAL.crnn_pretrained to your CRNN model path.
Change TRAIN.train_data_dir to your train data path.
Change TRAIN.VAL.val_data_dir to your val data path.
- train with textzoom
cd ./src/
python3 main.py --batch_size=1024 --STN --mask --gradient --vis_dir='vis'
- test with textzoom
python3 main.py --batch_size=1024 --test --test_data_dir='your-test-lmdb-dataset' --resume='your-model.pth' --STN --mask --gradient --vis_dir='vis'
- demo with images
python3 main.py --demo --demo_dir='./images/' --resume='your-model.pth' --STN --mask
TextZoom Dataset (allocated by size):
Paper: arxiv
Data (Lmdb): Badiu NetDisk. password: kybq; Google Drive
lmxb keys:
def buf2PIL(txn, key, type='RGB'):
imgbuf = txn.get(key)
buf = six.BytesIO()
buf.write(imgbuf)
buf.seek(0)
im = Image.open(buf).convert(type)
return im
nSamples = int(txn.get(b'num-samples'))
label_key = b'label-%09d' % index
word = str(txn.get(label_key).decode())
img_HR_key = b'image_hr-%09d' % index
img_lr_key = b'image_lr-%09d' % index
img_HR = buf2PIL(txn, img_HR_key, 'RGB')
img_lr = buf2PIL(txn, img_lr_key, 'RGB')
The LR images in TextZoom is much more challenging than synthetic LR images(BICUBIC).
We allocate our dataset into 3 part following difficulty: easy, medium and hard subset. The misalignment and ambiguity increases as the difficulty increases.
For each pair of LR-HR images, we provide the annotation of the case sensitive character string (including punctuation), the type of the bounding box, and the original focal lengths.
Other data
-
Cropped text images from SR_RAW (.png, allocated by original images): BaiduNet Disk. password: ykbq; Google Drive
-
Cropped text images from RealSR (.png, allocated by original images): BaiduNet Disk. password: f615; Google Drive
-
Annotation of SR_RAW (.json, bounding boxs and word labels): Baidu NetDisk. password: kmme; Google Drive
- Annotation of RealSR (.json bounding boxs and word labels): Baidu NetDisk. password: i52c; Google Drive
architecture of json: (sr_raw.json and real_sr.json have the same arch)
'position' is the bounding box,
'rawFileName' is the original image name, you need to download the SRRAW and RealSR dataset.
'words' is the word label.
'type' means the direction of bounding box, 'td' means top down, 'vn' means negative vertical (counterclockwise 90 degrees),
'vp' means positive vertical (clockwise 90 degrees), 'h' means horizontal.
with open('real_sr.json') as f:
d=json.load(f)
d['0']=
{'channal': '3',
'height': '2300',
'id': 'cbe0e4cba6ba6cd42d8ed4779087214a',
'polygons': {'wordRect':
[{'line-type': 'straight',
'position': [{'x': '247.94625', 'y': '186.31634'},
{'x': '99.29263', 'y': '186.60167'},
{'x': '99.29263', 'y': '165.77304'},
{'x': '247.94625', 'y': '166.34369'}],
'type': 'td',
'valid': 'true',
**'words': 'QU04029757'**},
{'line-type': 'straight',
'position': [{'x': '63.18353', 'y': '703.61181'},
{'x': '61.66713', 'y': '542.87290'},
{'x': '127.88347', 'y': '540.85103'},
{'x': '130.41081', 'y': '702.60087'}],
'type': 'vn',
'valid': 'true',
'words': '100'},
...
]},
'rawFilePath': 'test',
'rawFilename': 'Canon_046_HR.png',
'result_version': '1.0',
'rotate': '0',
'valid': 'true',
'width': '2500',
'wordRect-validity': 'true'}