https://github.com/bendangnuksung/Image-OutPainting

🏖 Keras Implementation of Painting outside the box
https://github.com/bendangnuksung/Image-OutPainting

computer-vision deep-learning gans image-outpainting keras python tensorflow

Last synced: 5 months ago
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🏖 Keras Implementation of Painting outside the box

• Host: GitHub
• URL: https://github.com/bendangnuksung/Image-OutPainting
• Owner: bendangnuksung
• Created: 2018-07-20T17:19:25.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-11-22T04:30:06.000Z (over 2 years ago)
• Last Synced: 2024-11-21T21:03:05.090Z (5 months ago)
• Topics: computer-vision, deep-learning, gans, image-outpainting, keras, python, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 483 KB
• Stars: 1,144
• Watchers: 43
• Forks: 228
• Open Issues: 12
• Metadata Files:
  • Readme: README.md

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README

        
# Keras implementation of Image OutPainting

This is an implementation of [Painting Outside the Box: Image Outpainting](https://cs230.stanford.edu/projects_spring_2018/posters/8265861.pdf) paper from Standford University. 

Some changes have been made to work with 256*256 image:

  - Added Identity loss i.e from generated image to the original image

  - Removed patches from training data. (training pipeline)

  - Replaced masking with cropping. (training pipeline)

  - Added convolution layers.

## Results

The model was train with [3500 scrapped beach data](https://drive.google.com/open?id=1hKIn-Z8Uf3voESbJZVsapLHESPabjjrb) with agumentation totalling upto 10500 images for 25 epochs.

![Demo](https://i.imgur.com/ZHtoeDF.jpg)

#### Recursive painting

![Demo](http://i.imgur.com/pDUpzcY.jpg)

### Install Requirements

```

sudo apt-get install curl

sudo pip3 install -r requirements.txt

```

## Get Started

1. Prepare Data:

      ```sh

      # Downloads the beach data and converts to numpy batch data

      # saves the Numpy batch data to 'data/prepared_data/'

      sh prepare_data.sh

      ```

2. Build Model

    * To build Model from scratch you can directly run 'outpaint.ipynb'

  
OR


    * You can [Download](https://drive.google.com/open?id=1MfXsRwjx5CTRGBoLx154S0h-Q3rIUNH0) my trained model and move it to 'checkpoint/' and run it.

## References

* [Painting Outside the Box: Image Outpainting](https://cs230.stanford.edu/projects_spring_2018/posters/8265861.pdf)