https://github.com/amir22010/deep-painterly-harmonization

Code and data for paper "Deep Painterly Harmonization": https://arxiv.org/abs/1804.03189

https://github.com/amir22010/deep-painterly-harmonization

Repository

Code and data for paper "Deep Painterly Harmonization": https://arxiv.org/abs/1804.03189

https://github.com/Amir22010/deep-painterly-harmonization/blob/master/

# deep-painterly-harmonization
Code and data for paper "[Deep Painterly Harmonization](https://arxiv.org/abs/1804.03189)"  

## Disclaimer 
**This software is published for academic and non-commercial use only.**

## Setup
This code is based on torch. It has been tested on Ubuntu 16.04 LTS.

Dependencies:
* [Torch](https://github.com/torch/torch7) (with [loadcaffe](https://github.com/szagoruyko/loadcaffe))
* [Matlab](https://www.mathworks.com/) or [Octave](https://www.gnu.org/software/octave/)

CUDA backend:
* [CUDA](https://developer.nvidia.com/cuda-downloads)
* [cudnn](https://developer.nvidia.com/cudnn)

Download VGG-19:
```
sh models/download_models.sh
```

Compile ``cuda_utils.cu`` (Adjust ``PREFIX`` and ``NVCC_PREFIX`` in ``makefile`` for your machine):
```
make clean && make
```

## Usage
To generate all results (in  ``data/``) using the provided scripts, simply run
```
python gen_all.py
```
in Python and then 
```
run('filt_cnn_artifact.m')
```
in Matlab or Octave. The final output will be in ``results/``.

Note that in the paper we trained a CNN on a dataset of 80,000 paintings collected from [wikiart.org](https://www.wikiart.org), which estimates the stylization level of a given painting and adjust weights accordingly. We will release the pre-trained model in the next update. Users will need to set those weights manually if running on their new paintings for now. 

**Removed a few images due to copyright issue. Full set [here](https://github.com/luanfujun/deep-painterly-harmonization/blob/master/README2.md) for testing use only.**
## Examples
Here are some results from our algorithm (from left to right are original painting, naive composite and our output):


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  



  
  
  





  
  
  




  
  
  



  
  
  


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  






  
  
  


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  


  
  
  




  
  
  



  
  
  


  
  
  


  
  
  


  
  
  






  
  
  



  
  
  



  
  
  



## Acknowledgement
* Our torch implementation is based on Justin Johnson's [code](https://github.com/jcjohnson/neural-style);
* Histogram loss is inspired by [Risser et al.](https://arxiv.org/abs/1701.08893)

## Citation
If you find this work useful for your research, please cite:
```
@article{luan2018deep,
  title={Deep Painterly Harmonization},
  author={Luan, Fujun and Paris, Sylvain and Shechtman, Eli and Bala, Kavita},
  journal={arXiv preprint arXiv:1804.03189},
  year={2018}
}
```

## Contact
Feel free to contact me if there is any question (Fujun Luan fl356@cornell.edu).

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