monet_boys's Introduction

Using machine learning to create drawings in the style of famous painters

Data analysis

Description: We analyzed various image datasets of painters to predict brushstrokes and train a model to transform a given photo.
Data Source: Model Zoo monet2photo, vangogh2photo, ukiyoe2photo, cezanne2photo: The art images were downloaded from Wikiart. The real photos are downloaded from Flickr using the combination of the tags landscape and landscapephotography. The training set size of each class is Monet:1074, Cezanne:584, Van Gogh:401, Ukiyo-e:1433, Photographs:6853.
Type of analysis: Implementing cycle GAN to generate the most accurate painting possible
Functional frontend can be found here

Startup the project

The initial setup.

Create virtualenv and install the project:

sudo apt-get install virtualenv python-pip python-dev
deactivate; virtualenv ~/venv ; source ~/venv/bin/activate ;\
    pip install pip -U; pip install -r requirements.txt

Unittest test:

make clean install test

Check for monet_boys in gitlab.com/{group}. If your project is not set please add it:

Create a new project on gitlab.com/{group}/monet_boys
Then populate it:

##   e.g. if group is "{group}" and project_name is "monet_boys"
git remote add origin [email protected]:{group}/monet_boys.git
git push -u origin master
git push -u origin --tags

Functionnal test with a script:

cd
mkdir tmp
cd tmp
monet_boys-run

Install

Go to https://github.com/{group}/monet_boys to see the project, manage issues, setup you ssh public key, ...

Create a python3 virtualenv and activate it:

sudo apt-get install virtualenv python-pip python-dev
deactivate; virtualenv -ppython3 ~/venv ; source ~/venv/bin/activate

Clone the project and install it:

git clone [email protected]:{group}/monet_boys.git
cd monet_boys
pip install -r requirements.txt
make clean install test                # install and test

Functionnal test with a script: