"Globally and Locally Consistent Image Completion"
Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa
ACM Transaction on Graphics (Proc. of SIGGRAPH 2017), 2017
This is an implementation of the image completion model proposed in the paper (Globally and Locally Consistent Image Completion) in Pytorch 0.4.
- Python 3
- Pytorch 0.4
- TensorbardX
- argparser
- etc (PIL, tqdm...)
This step is pre-pocessing of the image (make random mask) and transform image to torch tensor.
$ cd src_gl
$ python prepare_dataset2tensor.py
in github. I already uploaded "Facade dataset" with test and train. You can put any of datasets in data folder
And also you can change image size, maske size, maske margine
$ python prepare_dataset2tensor.py --patchSize 256 --patchSize 128 --boundary_margin 8
Train the "GL" model with pre-processed tensor data in step I.
$ cd src_gl
$ python train.py
$ cd src_gl
$ python eval.py
- Author Code (https://github.com/satoshiiizuka/siggraph2017_inpainting)
- GLCIC in tnesorflow (https://github.com/tadax/glcic)
- Code Base (https://github.com/pytorch/examples/tree/master/super_resolution)
Please cite the original paper in your publications:
@Article{IizukaSIGGRAPH2017,
author = {Satoshi Iizuka and Edgar Simo-Serra and Hiroshi Ishikawa},
title = {{Globally and Locally Consistent Image Completion}},
journal = "ACM Transactions on Graphics (Proc. of SIGGRAPH)",
year = 2017,
volume = 36,
number = 4,
pages = 107:1--107:14,
articleno = 107,
}
#Implementation Author chankyoo.moon / @dreamegg