devAmoghS / Keras-Style-Transfer

An implementation of "A Neural Algorithm of Artistic Style" in Keras

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Keras-Style-Transfer (KeSTra)

An implementation of "A Neural Algorithm of Artistic Style" (http://arxiv.org/abs/1508.06576) in Keras

The code present in this repository is presented in this blog.

The code is written in Keras 2.2.2

Preview

This is a 5-sec gif of Chicago city painted in the style of Rain Princess

Content Image and Style Image

Content Image Style Image

Installation notes

KeSTra is built using Python 3.5. The easiest way to set up a compatible environment is to use Conda. This will set up a virtual environment with the exact version of Python used for development along with all the dependencies needed to run KeSTra.

  1. Download and install Conda.
  2. Create a Conda environment with Python 3.

(Note: enter cd ~ to go on $HOME , then perform these commands)

```
conda create --name *your env name* python=3.5
```

You will get the following, kestra-test is the env name used in this example

Solving environment: done

## Package Plan ##

environment location: /home/user/anaconda3/envs/kestra-test

added / updated specs: 
 - python=3.5


The following NEW packages will be INSTALLED:

 ca-certificates: 2018.12.5-0            
 certifi:         2018.8.24-py35_1       
 libedit:         3.1.20181209-hc058e9b_0
 libffi:          3.2.1-hd88cf55_4       
 libgcc-ng:       8.2.0-hdf63c60_1       
 libstdcxx-ng:    8.2.0-hdf63c60_1       
 ncurses:         6.1-he6710b0_1         
 openssl:         1.0.2p-h14c3975_0      
 pip:             10.0.1-py35_0          
 python:          3.5.6-hc3d631a_0       
 readline:        7.0-h7b6447c_5         
 setuptools:      40.2.0-py35_0          
 sqlite:          3.26.0-h7b6447c_0      
 tk:              8.6.8-hbc83047_0       
 wheel:           0.31.1-py35_0          
 xz:              5.2.4-h14c3975_4       
 zlib:            1.2.11-h7b6447c_3      

Proceed ([y]/n)?  *Press y*

Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use:
# > source activate kestra-test
#
# To deactivate an active environment, use:
# > source deactivate
#

The environment is successfully created.

  1. Now activate the Conda environment.

    source activate *your env name*
    

    You will get the following

    (kestra-test) amogh@hp15X34:~$ 
    

    Enter conda list to get the list of available packages

        (kestra-test) amogh@hp15X34:~$ conda list
    # packages in environment at /home/amogh/anaconda3/envs/mlwp-test:
    #
    # Name                    Version                   Build  Channel
    ca-certificates           2018.12.5                     0  
    certifi                   2018.8.24                py35_1  
    libedit                   3.1.20181209         hc058e9b_0  
    libffi                    3.2.1                hd88cf55_4  
    libgcc-ng                 8.2.0                hdf63c60_1  
    libstdcxx-ng              8.2.0                hdf63c60_1  
    ncurses                   6.1                  he6710b0_1  
    openssl                   1.0.2p               h14c3975_0  
    pip                       10.0.1                   py35_0  
    python                    3.5.6                hc3d631a_0  
    readline                  7.0                  h7b6447c_5  
    setuptools                40.2.0                   py35_0  
    sqlite                    3.26.0               h7b6447c_0  
    tk                        8.6.8                hbc83047_0  
    wheel                     0.31.1                   py35_0  
    xz                        5.2.4                h14c3975_4  
    zlib                      1.2.11               h7b6447c_3 
    
  2. Install the required dependencies.

    (kestra-test) amogh@hp15X34:~$ conda install --yes --file *path to requirements.txt*
    
  3. In case you are not able to install the packages or getting PackagesNotFoundError Use the following command conda install -c conda-forge *list of packages separated by space*.

How good is the code ?

  • It is well tested
  • It passes style checks (PEP8 compliant)
  • It can compile in its current state (and there are relatively no issues)

How much support is available?

  • FAQs (coming soon)
  • Documentation (coming soon)

Issues

Feel free to submit issues and enhancement requests.

Contributing

Please refer to each project's style guidelines and guidelines for submitting patches and additions. In general, we follow the "fork-and-pull" Git workflow.

  1. Fork the repo on GitHub
  2. Clone the project to your own machine
  3. Commit changes to your own branch
  4. Push your work back up to your fork
  5. Submit a Pull request so that we can review your changes

NOTE: Be sure to merge the latest from "upstream" before making a pull request!

About

An implementation of "A Neural Algorithm of Artistic Style" in Keras

License:MIT License


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