Spaces:
Running
on
Zero
Running
on
Zero
A newer version of the Gradio SDK is available:
4.42.0
Dataset Preparation
The data files tree should be look like:
data/
eval/
dir300/
1_in.png
1_gt.png
...
kligler/
jung/
osr/
realdae/
docunet_docaligner/
dibco18/
train/
dewarping/
doc3d/
deshadowing/
fsdsrd/
tdd/
appearance/
clean_pdfs/
realdae/
deblurring/
tdd/
binarization/
bickly/
dibco/
noise_office/
phibd/
msi/
Evaluation Dataset
You can find the links for downloading the dataset we used for evaluation (Tables 1 and 2) in this repository, including DIR300 (300 samples), Kligler (300 samples), Jung (87 samples), OSR (237 samples), RealDAE (150 samples), DocUNet_DocAligner (150 samples), TDD (16000 samples) and DIBCO18 (10 samples). After downloading, add the suffix of _in
and _gt
to the input image and gt image respectively, and place them in the folder of the corresponding dataset
Training Dataset
You can find the links for downloading the dataset we used for training in this repository.
Dewarping
- Doc3D
- Mask extraction: you should extract the mask for each image from the uv data in Doc3D
- Background preparation: you can download the background data from here and specify it for self.background_paths in
loaders/docres_loader.py
- JSON preparation:
[
## you need to specify the paths of 'in_path', 'mask_path and 'gt_path':
{
"in_path": "dewarping/doc3d/img/1/102_1-pp_Page_048-xov0001.png",
"mask_path": "dewarping/doc3d/mask/1/102_1-pp_Page_048-xov0001.png",
"gt_path": "dewarping/doc3d/bm/1/102_1-pp_Page_048-xov0001.npy"
}
]
Deshadowing
- RDD
- FSDSRD
- JSON preparation
[ ## you need to specify the paths of 'in_path' and 'gt_path', for example:
{
"in_path": "deshadowing/fsdsrd/im/00004.png",
"gt_path": "deshadowing/fsdsrd/gt/00004.png"
},
{
"in_path": "deshadowing/rdd/im/00004.png",
"gt_path": "deshadowing/rdd/gt/00004.png"
}
]
Appearance enhancement
- Doc3DShade
- Clean PDFs collection: You should collection PDFs files from the internet and convert them as images to serve as the source for synthesis.
- Extract shadows from Doc3DShade by using
data/preprocess/shadow_extract.py
and dewarp the obtained shadows by usingdata/MBD/infer.py
. Then you should specify self.shadow_paths inloaders/docres_loader.py
- RealDAE
- JSON preparation:
[
## for Doc3DShade dataset, you only need to specify the path of image from PDF, for example:
{
'gt_path':'appearance/clean_pdfs/1.jpg'
},
## for RealDAE dataset, you need to specify the paths of both input and gt, for example:
{
'in_path': 'appearance/realdae/1_in.jpg',
'gt_path': 'appearance/realdae/1_gt.jpg'
}
]
Debluring
- TDD
- JSON preparation
[ ## you need to specify the paths of 'in_path' and 'gt_path', for example:
{
"in_path": "debluring/tdd/im/00004.png",
"gt_path": "debluring/tdd/gt/00004.png"
},
]
Binarization
- Bickly
- DTPrompt preparation: Since the DTPrompt for binarization is time-expensive, we obtain it offline before training. Use
data/preprocess/sauvola_binarize.py
- DTPrompt preparation: Since the DTPrompt for binarization is time-expensive, we obtain it offline before training. Use
- DIBCO
- DTPrompt preparation: the same as Bickly
- Noise Office
- DTPrompt preparation: the same as Bickly
- PHIDB
- DTPrompt preparation: the same as Bickly
- MSI
- DTPrompt preparation: the same as Bickly
- JSON preparation
[
## you need to specify the paths of 'in_path', 'gt_path', 'bin_path', 'thr_path' and 'gradient_path', for example:
{
"in_path": "binarization/noise_office/imgs/1.png",
"gt_path": "binarization/noise_office/gt_imgs/1.png",
"bin_path": "binarization/noise_office/imgs/1_bin.png",
"thr_path": "binarization/noise_office/imgs/1_thr.png",
"gradient_path": "binarization/noise_office/imgs/1_gradient.png"
},
]
After all the data are prepared, you should specify the dataset_setting in train.py
.