https://github.com/z-x-yang/ns-outpainting

Very Long Natural Scenery Image Prediction by Outpainting, ICCV2019, TensorFlow
https://github.com/z-x-yang/ns-outpainting

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Very Long Natural Scenery Image Prediction by Outpainting, ICCV2019, TensorFlow

• Host: GitHub
• URL: https://github.com/z-x-yang/ns-outpainting
• Owner: z-x-yang
• License: mit
• Created: 2019-11-12T07:48:48.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2021-02-02T14:25:06.000Z (almost 4 years ago)
• Last Synced: 2023-04-17T00:10:54.645Z (over 1 year ago)
• Language: Python
• Size: 3.81 MB
• Stars: 80
• Watchers: 6
• Forks: 15
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Very Long Natural Scenery Image Prediction by Outpainting (NS-Outpainting)

A neural architecture for scenery image outpaiting ([ICCV 2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_Very_Long_Natural_Scenery_Image_Prediction_by_Outpainting_ICCV_2019_paper.pdf)), implemented in [TensorFlow](http://www.tensorflow.org).

The architecture has an ability to generate a very long high-quality prediction from a small input image by outpaiting:







## Requirements and Preparation

Please install `TensorFlow>=1.3.0`, `Python>=3.6`.

For training and testing, we collect a new outpainting dataset, which has 6,000 images containing complex natural scenes. You can download the raw dataset from [here](https://drive.google.com/file/d/15rGKgeNHWqjs90An7wpZXJMz-zFaC1q0/view?usp=sharing) and split the training and testing set by yourself. Or, you can get our split from [here](https://drive.google.com/file/d/1LDRx0W6zo_eCZwN92pGgGZSCrqzB3KZ6/view?usp=sharing) (TFRecord format, 128 resolution, 5,000 images for training and 1,000 for testing).

## Usage

For training and evaluation, you can use [train.sh](/train.sh) and [eval.sh](/eval.sh). Please remember to set the TFRecord dataset path inside them.

Besides, you can get our **pretrain model** from [here](https://drive.google.com/file/d/1-DLSwNkB93MMKaYVO1rmPP9iJllXDJrg/view?usp=sharing), and run eval_model.py to evaluate it.

After running eval_model.py, the evaluation process will store 4 types of images: 

  1) "ori_xxx.jpg", the groundtruth images of size 128x256; 

  2) "m0_xxx.jpg", the 1-step predictions of size 128x256 without any post-processing methods;

  3) "m1_xxx.jpg", the 1-step predictions of size 128x256 with smoothly stitching;

  4) "endless_xxx.jpg", the 4-step predictions of size 128x640.

Notably, we measure Inception Score and Inception Distance between "ori_xxx.jpg" and "m0_xxx.jpg" in our paper.

## Citation

```

@inproceedings{yang2019very,

  title={Very Long Natural Scenery Image Prediction by Outpainting},

  author={Yang, Zongxin and Dong, Jian and Liu, Ping and Yang, Yi and Yan, Shuicheng},

  booktitle={Proceedings of the IEEE International Conference on Computer Vision},

  pages={10561--10570},

  year={2019}

}

```